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{"id": "finance_0000001", "data_type": "text", "content": {"input": "Explain behavioral biases taxonomy in operations (educational).", "output": "Behavioral Biases Taxonomy is a foundational concept in business (operations). Core principles include: agent incentives and moral hazard, network externalities and scale effects, aggregate demand-supply interactions. It is often expressed by relations such as CAPM: E[R] = Rf + β (E[Rm]-Rf) and applied in contexts like operational process optimization."}, "annotations": {"semantic_labels": [{"object": "behavioral_biases_taxonomy", "category": "operations", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "behavioral_biases_taxonomy", "relation": "relates_to_domain", "entity_2": "business"}], "intent_sentiment": {"intent": "technical_explanation", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of behavioral biases taxonomy.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "medium_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.86, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "business", "subdomain": "operations", "topic": "behavioral_biases_taxonomy"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Behavioral Biases Taxonomy", "description": "Behavioral Biases Taxonomy explained briefly: Behavioral Biases Taxonomy is a foundational concept in business (operations). Core principles include: agent incentives and moral hazard, network externalities and scale effects, aggregate demand-supply interactions. It is often expressed by relations such as CAPM: E[R] = Rf + β (E[Rm]-Rf) and applied in contexts like operational process optimization. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["agent incentives and moral hazard", "network externalities and scale effects", "aggregate demand-supply interactions", "trade-offs between risk and return", "inventory flow balancing"], "mathematical_formulas": ["CAPM: E[R] = Rf + β (E[Rm]-Rf)"], "key_applications": ["operational process optimization", "credit scoring and loan provisioning"], "teaching_examples": ["Example: compute NPV of a three-year project with cashflows [100,200,300]."], "difficulty_level": "advanced", "estimated_read_time_min": 18, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:07:59.613935Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000002", "data_type": "text", "content": {"input": "Explain capital asset pricing in corporate_finance (educational).", "output": "Capital Asset Pricing is a foundational concept in finance (corporate finance). Core principles include: trade-offs between risk and return, agent incentives and moral hazard, marginal analysis. It is often expressed by relations such as Leontief Input-Output: x = (I - A)^{-1} f and applied in contexts like pricing of derivatives and structured products."}, "annotations": {"semantic_labels": [{"object": "capital_asset_pricing", "category": "corporate_finance", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "capital_asset_pricing", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "educational_summary", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of capital asset pricing.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.96, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "corporate_finance", "topic": "capital_asset_pricing"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Capital Asset Pricing", "description": "Capital Asset Pricing explained briefly: Capital Asset Pricing is a foundational concept in finance (corporate finance). Core principles include: trade-offs between risk and return, agent incentives and moral hazard, marginal analysis. It is often expressed by relations such as Leontief Input-Output: x = (I - A)^{-1} f and applied in contexts like pricing of derivatives and structured products. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["trade-offs between risk and return", "agent incentives and moral hazard", "marginal analysis", "network externalities and scale effects", "optimization under constraints"], "mathematical_formulas": ["Leontief Input-Output: x = (I - A)^{-1} f"], "key_applications": ["pricing of derivatives and structured products", "policy design and macro forecasting"], "teaching_examples": ["Example: compare two portfolios using Sharpe ratio with returns 0.12 and 0.08."], "difficulty_level": "advanced", "estimated_read_time_min": 6, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:07:59.731275Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000003", "data_type": "text", "content": {"input": "Explain credit risk model in investments (educational).", "output": "Credit Risk Model is a foundational concept in finance (investments). Core principles include: inventory flow balancing, information asymmetry effects, optimization under constraints. It is often expressed by relations such as NPV = Σ (Ct / (1 + r)^t) - C0 and applied in contexts like valuation of firms and projects."}, "annotations": {"semantic_labels": [{"object": "credit_risk_model", "category": "investments", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "credit_risk_model", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "application_example", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of credit risk model.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "long_term", "stability": "volatile"}, "quality_confidence": {"confidence_score": 0.93, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "investments", "topic": "credit_risk_model"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Credit Risk Model", "description": "Credit Risk Model explained briefly: Credit Risk Model is a foundational concept in finance (investments). Core principles include: inventory flow balancing, information asymmetry effects, optimization under constraints. It is often expressed by relations such as NPV = Σ (Ct / (1 + r)^t) - C0 and applied in contexts like valuation of firms and projects. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["inventory flow balancing", "information asymmetry effects", "optimization under constraints", "discounting future streams", "network externalities and scale effects"], "mathematical_formulas": ["NPV = Σ (Ct / (1 + r)^t) - C0"], "key_applications": ["valuation of firms and projects", "behavioral interventions for consumer finance"], "teaching_examples": ["Example: compare two portfolios using Sharpe ratio with returns 0.12 and 0.08."], "difficulty_level": "advanced", "estimated_read_time_min": 5, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:07:59.761702Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000004", "data_type": "text", "content": {"input": "Explain arbitrage pricing in corporate_finance (educational).", "output": "Arbitrage Pricing is a foundational concept in finance (corporate finance). Core principles include: marginal analysis, market clearing via price adjustments, inventory flow balancing. It is often expressed by relations such as PV = FV / (1 + r)^n and applied in contexts like pricing of derivatives and structured products."}, "annotations": {"semantic_labels": [{"object": "arbitrage_pricing", "category": "corporate_finance", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "arbitrage_pricing", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "educational_summary", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of arbitrage pricing.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "stable"}, "quality_confidence": {"confidence_score": 0.9, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "corporate_finance", "topic": "arbitrage_pricing"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Arbitrage Pricing", "description": "Arbitrage Pricing explained briefly: Arbitrage Pricing is a foundational concept in finance (corporate finance). Core principles include: marginal analysis, market clearing via price adjustments, inventory flow balancing. It is often expressed by relations such as PV = FV / (1 + r)^n and applied in contexts like pricing of derivatives and structured products. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["marginal analysis", "market clearing via price adjustments", "inventory flow balancing", "network externalities and scale effects", "information asymmetry effects"], "mathematical_formulas": ["PV = FV / (1 + r)^n"], "key_applications": ["pricing of derivatives and structured products", "valuation of firms and projects"], "teaching_examples": ["Example: compare two portfolios using Sharpe ratio with returns 0.12 and 0.08."], "difficulty_level": "introductory", "estimated_read_time_min": 20, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:07:59.793243Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000005", "data_type": "text", "content": {"input": "Explain network effects in financial_markets (educational).", "output": "Network Effects is a foundational concept in finance (financial markets). Core principles include: stochastic shocks and mean reversion, trade-offs between risk and return, discounting future streams. It is often expressed by relations such as CAPM: E[R] = Rf + β (E[Rm]-Rf) and applied in contexts like risk management and hedging strategies."}, "annotations": {"semantic_labels": [{"object": "network_effects", "category": "financial_markets", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "network_effects", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "application_example", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of network effects.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.95, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "financial_markets", "topic": "network_effects"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Network Effects", "description": "Network Effects explained briefly: Network Effects is a foundational concept in finance (financial markets). Core principles include: stochastic shocks and mean reversion, trade-offs between risk and return, discounting future streams. It is often expressed by relations such as CAPM: E[R] = Rf + β (E[Rm]-Rf) and applied in contexts like risk management and hedging strategies. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["stochastic shocks and mean reversion", "trade-offs between risk and return", "discounting future streams", "information asymmetry effects", "inventory flow balancing"], "mathematical_formulas": ["CAPM: E[R] = Rf + β (E[Rm]-Rf)"], "key_applications": ["risk management and hedging strategies", "credit scoring and loan provisioning"], "teaching_examples": ["Example: apply supply-demand shift to simulate tax incidence."], "difficulty_level": "intermediate", "estimated_read_time_min": 5, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:07:59.826282Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000006", "data_type": "text", "content": {"input": "Explain modigliani-miller in corporate_finance (educational).", "output": "Modigliani-Miller is a foundational concept in finance (corporate finance). Core principles include: stochastic shocks and mean reversion, marginal analysis, discounting future streams. It is often expressed by relations such as PV = FV / (1 + r)^n and applied in contexts like risk management and hedging strategies."}, "annotations": {"semantic_labels": [{"object": "modigliani-miller", "category": "corporate_finance", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "modigliani-miller", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "technical_explanation", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of modigliani-miller.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "stable"}, "quality_confidence": {"confidence_score": 0.96, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "corporate_finance", "topic": "modigliani-miller"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Modigliani-Miller", "description": "Modigliani-Miller explained briefly: Modigliani-Miller is a foundational concept in finance (corporate finance). Core principles include: stochastic shocks and mean reversion, marginal analysis, discounting future streams. It is often expressed by relations such as PV = FV / (1 + r)^n and applied in contexts like risk management and hedging strategies. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["stochastic shocks and mean reversion", "marginal analysis", "discounting future streams", "trade-offs between risk and return", "network externalities and scale effects"], "mathematical_formulas": ["PV = FV / (1 + r)^n"], "key_applications": ["risk management and hedging strategies", "supply chain inventory optimization"], "teaching_examples": ["Example: compute NPV of a three-year project with cashflows [100,200,300]."], "difficulty_level": "advanced", "estimated_read_time_min": 6, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:07:59.849677Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000007", "data_type": "text", "content": {"input": "Explain fiscal multiplier in financial_markets (educational).", "output": "Fiscal Multiplier is a foundational concept in finance (financial markets). Core principles include: information asymmetry effects, aggregate demand-supply interactions, behavioral bias corrections. It is often expressed by relations such as Leontief Input-Output: x = (I - A)^{-1} f and applied in contexts like market regulation and compliance."}, "annotations": {"semantic_labels": [{"object": "fiscal_multiplier", "category": "financial_markets", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "fiscal_multiplier", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "educational_summary", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of fiscal multiplier.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "medium_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.95, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "financial_markets", "topic": "fiscal_multiplier"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Fiscal Multiplier", "description": "Fiscal Multiplier explained briefly: Fiscal Multiplier is a foundational concept in finance (financial markets). Core principles include: information asymmetry effects, aggregate demand-supply interactions, behavioral bias corrections. It is often expressed by relations such as Leontief Input-Output: x = (I - A)^{-1} f and applied in contexts like market regulation and compliance. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["information asymmetry effects", "aggregate demand-supply interactions", "behavioral bias corrections", "optimization under constraints", "marginal analysis"], "mathematical_formulas": ["Leontief Input-Output: x = (I - A)^{-1} f"], "key_applications": ["market regulation and compliance", "credit scoring and loan provisioning"], "teaching_examples": ["Example: compute NPV of a three-year project with cashflows [100,200,300]."], "difficulty_level": "introductory", "estimated_read_time_min": 9, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:07:59.892332Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000008", "data_type": "text", "content": {"input": "Explain stress testing in time_series (educational).", "output": "Stress Testing is a foundational concept in econometrics (time series). Core principles include: stochastic shocks and mean reversion, network externalities and scale effects, inventory flow balancing. It is often expressed by relations such as IRR: NPV(IRR) = 0 and applied in contexts like credit scoring and loan provisioning."}, "annotations": {"semantic_labels": [{"object": "stress_testing", "category": "time_series", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "stress_testing", "relation": "relates_to_domain", "entity_2": "econometrics"}], "intent_sentiment": {"intent": "educational_summary", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of stress testing.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "long_term", "stability": "volatile"}, "quality_confidence": {"confidence_score": 0.96, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "econometrics", "subdomain": "time_series", "topic": "stress_testing"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Stress Testing", "description": "Stress Testing explained briefly: Stress Testing is a foundational concept in econometrics (time series). Core principles include: stochastic shocks and mean reversion, network externalities and scale effects, inventory flow balancing. It is often expressed by relations such as IRR: NPV(IRR) = 0 and applied in contexts like credit scoring and loan provisioning. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["stochastic shocks and mean reversion", "network externalities and scale effects", "inventory flow balancing", "opportunity cost reasoning", "agent incentives and moral hazard"], "mathematical_formulas": ["IRR: NPV(IRR) = 0"], "key_applications": ["credit scoring and loan provisioning", "behavioral interventions for consumer finance"], "teaching_examples": ["Example: apply supply-demand shift to simulate tax incidence."], "difficulty_level": "intermediate", "estimated_read_time_min": 17, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:07:59.934644Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000009", "data_type": "text", "content": {"input": "Explain transaction cost economics in macroeconomics (educational).", "output": "Transaction Cost Economics is a foundational concept in economics (macroeconomics). Core principles include: behavioral bias corrections, information asymmetry effects, agent incentives and moral hazard. It is often expressed by relations such as IRR: NPV(IRR) = 0 and applied in contexts like risk management and hedging strategies."}, "annotations": {"semantic_labels": [{"object": "transaction_cost_economics", "category": "macroeconomics", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "transaction_cost_economics", "relation": "relates_to_domain", "entity_2": "economics"}], "intent_sentiment": {"intent": "model_specification", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of transaction cost economics.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "medium_term", "stability": "volatile"}, "quality_confidence": {"confidence_score": 0.9, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "economics", "subdomain": "macroeconomics", "topic": "transaction_cost_economics"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Transaction Cost Economics", "description": "Transaction Cost Economics explained briefly: Transaction Cost Economics is a foundational concept in economics (macroeconomics). Core principles include: behavioral bias corrections, information asymmetry effects, agent incentives and moral hazard. It is often expressed by relations such as IRR: NPV(IRR) = 0 and applied in contexts like risk management and hedging strategies. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["behavioral bias corrections", "information asymmetry effects", "agent incentives and moral hazard", "market clearing via price adjustments", "optimization under constraints"], "mathematical_formulas": ["IRR: NPV(IRR) = 0"], "key_applications": ["risk management and hedging strategies", "market regulation and compliance"], "teaching_examples": ["Example: demonstrate arbitrage between two markets with price gap 0.02."], "difficulty_level": "advanced", "estimated_read_time_min": 14, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:07:59.987786Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000010", "data_type": "text", "content": {"input": "Explain platform economics in strategy (educational).", "output": "Platform Economics is a foundational concept in business (strategy). Core principles include: inventory flow balancing, marginal analysis, trade-offs between risk and return. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like behavioral interventions for consumer finance."}, "annotations": {"semantic_labels": [{"object": "platform_economics", "category": "strategy", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "platform_economics", "relation": "relates_to_domain", "entity_2": "business"}], "intent_sentiment": {"intent": "model_specification", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of platform economics.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "medium_term", "stability": "stable"}, "quality_confidence": {"confidence_score": 0.88, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "business", "subdomain": "strategy", "topic": "platform_economics"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Platform Economics", "description": "Platform Economics explained briefly: Platform Economics is a foundational concept in business (strategy). Core principles include: inventory flow balancing, marginal analysis, trade-offs between risk and return. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like behavioral interventions for consumer finance. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["inventory flow balancing", "marginal analysis", "trade-offs between risk and return", "behavioral bias corrections", "discounting future streams"], "mathematical_formulas": ["DebtRatio = Debt / (Debt + Equity)"], "key_applications": ["behavioral interventions for consumer finance", "pricing of derivatives and structured products"], "teaching_examples": ["Example: compare two portfolios using Sharpe ratio with returns 0.12 and 0.08."], "difficulty_level": "intermediate", "estimated_read_time_min": 12, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.044435Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000011", "data_type": "text", "content": {"input": "Explain banking frictions in corporate_finance (educational).", "output": "Banking Frictions is a foundational concept in finance (corporate finance). Core principles include: optimization under constraints, network externalities and scale effects, trade-offs between risk and return. It is often expressed by relations such as NPV = Σ (Ct / (1 + r)^t) - C0 and applied in contexts like pricing of derivatives and structured products."}, "annotations": {"semantic_labels": [{"object": "banking_frictions", "category": "corporate_finance", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "banking_frictions", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "educational_summary", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of banking frictions.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "medium_term", "stability": "volatile"}, "quality_confidence": {"confidence_score": 0.87, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "corporate_finance", "topic": "banking_frictions"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Banking Frictions", "description": "Banking Frictions explained briefly: Banking Frictions is a foundational concept in finance (corporate finance). Core principles include: optimization under constraints, network externalities and scale effects, trade-offs between risk and return. It is often expressed by relations such as NPV = Σ (Ct / (1 + r)^t) - C0 and applied in contexts like pricing of derivatives and structured products. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["optimization under constraints", "network externalities and scale effects", "trade-offs between risk and return", "information asymmetry effects", "stochastic shocks and mean reversion"], "mathematical_formulas": ["NPV = Σ (Ct / (1 + r)^t) - C0"], "key_applications": ["pricing of derivatives and structured products", "asset allocation and portfolio rebalancing"], "teaching_examples": ["Example: compare two portfolios using Sharpe ratio with returns 0.12 and 0.08."], "difficulty_level": "advanced", "estimated_read_time_min": 16, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.076323Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000012", "data_type": "text", "content": {"input": "Explain principal-agent in financial_accounting (educational).", "output": "Principal-Agent is a foundational concept in accounting (financial accounting). Core principles include: trade-offs between risk and return, information asymmetry effects, network externalities and scale effects. It is often expressed by relations such as Leontief Input-Output: x = (I - A)^{-1} f and applied in contexts like asset allocation and portfolio rebalancing."}, "annotations": {"semantic_labels": [{"object": "principal-agent", "category": "financial_accounting", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "principal-agent", "relation": "relates_to_domain", "entity_2": "accounting"}], "intent_sentiment": {"intent": "technical_explanation", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of principal-agent.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "long_term", "stability": "stable"}, "quality_confidence": {"confidence_score": 0.92, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "accounting", "subdomain": "financial_accounting", "topic": "principal-agent"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Principal-Agent", "description": "Principal-Agent explained briefly: Principal-Agent is a foundational concept in accounting (financial accounting). Core principles include: trade-offs between risk and return, information asymmetry effects, network externalities and scale effects. It is often expressed by relations such as Leontief Input-Output: x = (I - A)^{-1} f and applied in contexts like asset allocation and portfolio rebalancing. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["trade-offs between risk and return", "information asymmetry effects", "network externalities and scale effects", "agent incentives and moral hazard", "stochastic shocks and mean reversion"], "mathematical_formulas": ["Leontief Input-Output: x = (I - A)^{-1} f"], "key_applications": ["asset allocation and portfolio rebalancing", "market regulation and compliance"], "teaching_examples": ["Example: demonstrate arbitrage between two markets with price gap 0.02."], "difficulty_level": "advanced", "estimated_read_time_min": 5, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.101234Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000013", "data_type": "text", "content": {"input": "Explain discounted cash flow in financial_markets (educational).", "output": "Discounted Cash Flow is a foundational concept in finance (financial markets). Core principles include: network externalities and scale effects, trade-offs between risk and return, market clearing via price adjustments. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like risk management and hedging strategies."}, "annotations": {"semantic_labels": [{"object": "discounted_cash_flow", "category": "financial_markets", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "discounted_cash_flow", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "technical_explanation", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of discounted cash flow.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "medium_term", "stability": "volatile"}, "quality_confidence": {"confidence_score": 0.93, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "financial_markets", "topic": "discounted_cash_flow"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Discounted Cash Flow", "description": "Discounted Cash Flow explained briefly: Discounted Cash Flow is a foundational concept in finance (financial markets). Core principles include: network externalities and scale effects, trade-offs between risk and return, market clearing via price adjustments. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like risk management and hedging strategies. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["network externalities and scale effects", "trade-offs between risk and return", "market clearing via price adjustments", "marginal analysis", "information asymmetry effects"], "mathematical_formulas": ["DebtRatio = Debt / (Debt + Equity)"], "key_applications": ["risk management and hedging strategies", "asset allocation and portfolio rebalancing"], "teaching_examples": ["Example: apply supply-demand shift to simulate tax incidence."], "difficulty_level": "introductory", "estimated_read_time_min": 13, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.139505Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000014", "data_type": "text", "content": {"input": "Explain currency carry trade in corporate_finance (educational).", "output": "Currency Carry Trade is a foundational concept in finance (corporate finance). Core principles include: aggregate demand-supply interactions, network externalities and scale effects, market clearing via price adjustments. It is often expressed by relations such as IRR: NPV(IRR) = 0 and applied in contexts like credit scoring and loan provisioning."}, "annotations": {"semantic_labels": [{"object": "currency_carry_trade", "category": "corporate_finance", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "currency_carry_trade", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "model_specification", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of currency carry trade.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "long_term", "stability": "stable"}, "quality_confidence": {"confidence_score": 0.99, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "corporate_finance", "topic": "currency_carry_trade"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Currency Carry Trade", "description": "Currency Carry Trade explained briefly: Currency Carry Trade is a foundational concept in finance (corporate finance). Core principles include: aggregate demand-supply interactions, network externalities and scale effects, market clearing via price adjustments. It is often expressed by relations such as IRR: NPV(IRR) = 0 and applied in contexts like credit scoring and loan provisioning. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["aggregate demand-supply interactions", "network externalities and scale effects", "market clearing via price adjustments", "marginal analysis", "opportunity cost reasoning"], "mathematical_formulas": ["IRR: NPV(IRR) = 0"], "key_applications": ["credit scoring and loan provisioning", "asset allocation and portfolio rebalancing"], "teaching_examples": ["Example: demonstrate arbitrage between two markets with price gap 0.02."], "difficulty_level": "advanced", "estimated_read_time_min": 20, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.177822Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000015", "data_type": "text", "content": {"input": "Explain cost-benefit analysis in corporate_finance (educational).", "output": "Cost-Benefit Analysis is a foundational concept in finance (corporate finance). Core principles include: market clearing via price adjustments, agent incentives and moral hazard, behavioral bias corrections. It is often expressed by relations such as NPV = Σ (Ct / (1 + r)^t) - C0 and applied in contexts like market regulation and compliance."}, "annotations": {"semantic_labels": [{"object": "cost-benefit_analysis", "category": "corporate_finance", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "cost-benefit_analysis", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "technical_explanation", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of cost-benefit analysis.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "long_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.96, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "corporate_finance", "topic": "cost-benefit_analysis"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Cost-Benefit Analysis", "description": "Cost-Benefit Analysis explained briefly: Cost-Benefit Analysis is a foundational concept in finance (corporate finance). Core principles include: market clearing via price adjustments, agent incentives and moral hazard, behavioral bias corrections. It is often expressed by relations such as NPV = Σ (Ct / (1 + r)^t) - C0 and applied in contexts like market regulation and compliance. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["market clearing via price adjustments", "agent incentives and moral hazard", "behavioral bias corrections", "information asymmetry effects", "optimization under constraints"], "mathematical_formulas": ["NPV = Σ (Ct / (1 + r)^t) - C0"], "key_applications": ["market regulation and compliance", "operational process optimization"], "teaching_examples": ["Example: apply supply-demand shift to simulate tax incidence."], "difficulty_level": "intermediate", "estimated_read_time_min": 9, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.219585Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000016", "data_type": "text", "content": {"input": "Explain liquidity preference in strategy (educational).", "output": "Liquidity Preference is a foundational concept in business (strategy). Core principles include: inventory flow balancing, aggregate demand-supply interactions, trade-offs between risk and return. It is often expressed by relations such as Sharpe = (Rp - Rf) / σp and applied in contexts like operational process optimization."}, "annotations": {"semantic_labels": [{"object": "liquidity_preference", "category": "strategy", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "liquidity_preference", "relation": "relates_to_domain", "entity_2": "business"}], "intent_sentiment": {"intent": "technical_explanation", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of liquidity preference.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "medium_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.96, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "business", "subdomain": "strategy", "topic": "liquidity_preference"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Liquidity Preference", "description": "Liquidity Preference explained briefly: Liquidity Preference is a foundational concept in business (strategy). Core principles include: inventory flow balancing, aggregate demand-supply interactions, trade-offs between risk and return. It is often expressed by relations such as Sharpe = (Rp - Rf) / σp and applied in contexts like operational process optimization. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["inventory flow balancing", "aggregate demand-supply interactions", "trade-offs between risk and return", "network externalities and scale effects", "behavioral bias corrections"], "mathematical_formulas": ["Sharpe = (Rp - Rf) / σp"], "key_applications": ["operational process optimization", "valuation of firms and projects"], "teaching_examples": ["Example: compare two portfolios using Sharpe ratio with returns 0.12 and 0.08."], "difficulty_level": "advanced", "estimated_read_time_min": 10, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.253958Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000017", "data_type": "text", "content": {"input": "Explain capital structure in behavioral_finance (educational).", "output": "Capital Structure is a foundational concept in behavioral (behavioral finance). Core principles include: network externalities and scale effects, aggregate demand-supply interactions, stochastic shocks and mean reversion. It is often expressed by relations such as Leontief Input-Output: x = (I - A)^{-1} f and applied in contexts like operational process optimization."}, "annotations": {"semantic_labels": [{"object": "capital_structure", "category": "behavioral_finance", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "capital_structure", "relation": "relates_to_domain", "entity_2": "behavioral"}], "intent_sentiment": {"intent": "educational_summary", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of capital structure.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "medium_term", "stability": "stable"}, "quality_confidence": {"confidence_score": 0.96, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "behavioral", "subdomain": "behavioral_finance", "topic": "capital_structure"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Capital Structure", "description": "Capital Structure explained briefly: Capital Structure is a foundational concept in behavioral (behavioral finance). Core principles include: network externalities and scale effects, aggregate demand-supply interactions, stochastic shocks and mean reversion. It is often expressed by relations such as Leontief Input-Output: x = (I - A)^{-1} f and applied in contexts like operational process optimization. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["network externalities and scale effects", "aggregate demand-supply interactions", "stochastic shocks and mean reversion", "trade-offs between risk and return", "optimization under constraints"], "mathematical_formulas": ["Leontief Input-Output: x = (I - A)^{-1} f"], "key_applications": ["operational process optimization", "risk management and hedging strategies"], "teaching_examples": ["Example: compare two portfolios using Sharpe ratio with returns 0.12 and 0.08."], "difficulty_level": "advanced", "estimated_read_time_min": 19, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.301764Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000018", "data_type": "text", "content": {"input": "Explain comparative advantage in operations (educational).", "output": "Comparative Advantage is a foundational concept in business (operations). Core principles include: marginal analysis, opportunity cost reasoning, trade-offs between risk and return. It is often expressed by relations such as CAPM: E[R] = Rf + β (E[Rm]-Rf) and applied in contexts like risk management and hedging strategies."}, "annotations": {"semantic_labels": [{"object": "comparative_advantage", "category": "operations", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "comparative_advantage", "relation": "relates_to_domain", "entity_2": "business"}], "intent_sentiment": {"intent": "application_example", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of comparative advantage.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "medium_term", "stability": "stable"}, "quality_confidence": {"confidence_score": 0.89, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "business", "subdomain": "operations", "topic": "comparative_advantage"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Comparative Advantage", "description": "Comparative Advantage explained briefly: Comparative Advantage is a foundational concept in business (operations). Core principles include: marginal analysis, opportunity cost reasoning, trade-offs between risk and return. It is often expressed by relations such as CAPM: E[R] = Rf + β (E[Rm]-Rf) and applied in contexts like risk management and hedging strategies. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["marginal analysis", "opportunity cost reasoning", "trade-offs between risk and return", "information asymmetry effects", "network externalities and scale effects"], "mathematical_formulas": ["CAPM: E[R] = Rf + β (E[Rm]-Rf)"], "key_applications": ["risk management and hedging strategies", "asset allocation and portfolio rebalancing"], "teaching_examples": ["Example: apply supply-demand shift to simulate tax incidence."], "difficulty_level": "intermediate", "estimated_read_time_min": 18, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.358265Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000019", "data_type": "text", "content": {"input": "Explain yield curve theory in financial_markets (educational).", "output": "Yield Curve Theory is a foundational concept in finance (financial markets). Core principles include: network externalities and scale effects, behavioral bias corrections, marginal analysis. It is often expressed by relations such as NPV = Σ (Ct / (1 + r)^t) - C0 and applied in contexts like operational process optimization."}, "annotations": {"semantic_labels": [{"object": "yield_curve_theory", "category": "financial_markets", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "yield_curve_theory", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "model_specification", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of yield curve theory.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.86, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "financial_markets", "topic": "yield_curve_theory"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Yield Curve Theory", "description": "Yield Curve Theory explained briefly: Yield Curve Theory is a foundational concept in finance (financial markets). Core principles include: network externalities and scale effects, behavioral bias corrections, marginal analysis. It is often expressed by relations such as NPV = Σ (Ct / (1 + r)^t) - C0 and applied in contexts like operational process optimization. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["network externalities and scale effects", "behavioral bias corrections", "marginal analysis", "agent incentives and moral hazard", "aggregate demand-supply interactions"], "mathematical_formulas": ["NPV = Σ (Ct / (1 + r)^t) - C0"], "key_applications": ["operational process optimization", "asset allocation and portfolio rebalancing"], "teaching_examples": ["Example: demonstrate arbitrage between two markets with price gap 0.02."], "difficulty_level": "intermediate", "estimated_read_time_min": 17, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.406345Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000020", "data_type": "text", "content": {"input": "Explain agency theory in financial_accounting (educational).", "output": "Agency Theory is a foundational concept in accounting (financial accounting). Core principles include: market clearing via price adjustments, discounting future streams, behavioral bias corrections. It is often expressed by relations such as Leontief Input-Output: x = (I - A)^{-1} f and applied in contexts like credit scoring and loan provisioning."}, "annotations": {"semantic_labels": [{"object": "agency_theory", "category": "financial_accounting", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "agency_theory", "relation": "relates_to_domain", "entity_2": "accounting"}], "intent_sentiment": {"intent": "model_specification", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of agency theory.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "stable"}, "quality_confidence": {"confidence_score": 0.86, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "accounting", "subdomain": "financial_accounting", "topic": "agency_theory"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Agency Theory", "description": "Agency Theory explained briefly: Agency Theory is a foundational concept in accounting (financial accounting). Core principles include: market clearing via price adjustments, discounting future streams, behavioral bias corrections. It is often expressed by relations such as Leontief Input-Output: x = (I - A)^{-1} f and applied in contexts like credit scoring and loan provisioning. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["market clearing via price adjustments", "discounting future streams", "behavioral bias corrections", "network externalities and scale effects", "agent incentives and moral hazard"], "mathematical_formulas": ["Leontief Input-Output: x = (I - A)^{-1} f"], "key_applications": ["credit scoring and loan provisioning", "pricing of derivatives and structured products"], "teaching_examples": ["Example: stress-test a bank's portfolio under 30% market drop."], "difficulty_level": "advanced", "estimated_read_time_min": 20, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.453453Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000021", "data_type": "text", "content": {"input": "Explain capital asset pricing in microeconomics (educational).", "output": "Capital Asset Pricing is a foundational concept in economics (microeconomics). Core principles include: marginal analysis, discounting future streams, trade-offs between risk and return. It is often expressed by relations such as CAPM: E[R] = Rf + β (E[Rm]-Rf) and applied in contexts like behavioral interventions for consumer finance."}, "annotations": {"semantic_labels": [{"object": "capital_asset_pricing", "category": "microeconomics", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "capital_asset_pricing", "relation": "relates_to_domain", "entity_2": "economics"}], "intent_sentiment": {"intent": "educational_summary", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of capital asset pricing.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "long_term", "stability": "stable"}, "quality_confidence": {"confidence_score": 0.95, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "economics", "subdomain": "microeconomics", "topic": "capital_asset_pricing"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Capital Asset Pricing", "description": "Capital Asset Pricing explained briefly: Capital Asset Pricing is a foundational concept in economics (microeconomics). Core principles include: marginal analysis, discounting future streams, trade-offs between risk and return. It is often expressed by relations such as CAPM: E[R] = Rf + β (E[Rm]-Rf) and applied in contexts like behavioral interventions for consumer finance. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["marginal analysis", "discounting future streams", "trade-offs between risk and return", "stochastic shocks and mean reversion", "optimization under constraints"], "mathematical_formulas": ["CAPM: E[R] = Rf + β (E[Rm]-Rf)"], "key_applications": ["behavioral interventions for consumer finance", "supply chain inventory optimization"], "teaching_examples": ["Example: demonstrate arbitrage between two markets with price gap 0.02."], "difficulty_level": "advanced", "estimated_read_time_min": 6, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.502199Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000022", "data_type": "text", "content": {"input": "Explain time value of money in macroeconomics (educational).", "output": "Time Value Of Money is a foundational concept in economics (macroeconomics). Core principles include: aggregate demand-supply interactions, market clearing via price adjustments, agent incentives and moral hazard. It is often expressed by relations such as CAPM: E[R] = Rf + β (E[Rm]-Rf) and applied in contexts like market regulation and compliance."}, "annotations": {"semantic_labels": [{"object": "time_value_of_money", "category": "macroeconomics", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "time_value_of_money", "relation": "relates_to_domain", "entity_2": "economics"}], "intent_sentiment": {"intent": "educational_summary", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of time value of money.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.98, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "economics", "subdomain": "macroeconomics", "topic": "time_value_of_money"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Time Value Of Money", "description": "Time Value Of Money explained briefly: Time Value Of Money is a foundational concept in economics (macroeconomics). Core principles include: aggregate demand-supply interactions, market clearing via price adjustments, agent incentives and moral hazard. It is often expressed by relations such as CAPM: E[R] = Rf + β (E[Rm]-Rf) and applied in contexts like market regulation and compliance. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["aggregate demand-supply interactions", "market clearing via price adjustments", "agent incentives and moral hazard", "stochastic shocks and mean reversion", "network externalities and scale effects"], "mathematical_formulas": ["CAPM: E[R] = Rf + β (E[Rm]-Rf)"], "key_applications": ["market regulation and compliance", "behavioral interventions for consumer finance"], "teaching_examples": ["Example: apply supply-demand shift to simulate tax incidence."], "difficulty_level": "intermediate", "estimated_read_time_min": 4, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.549269Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000023", "data_type": "text", "content": {"input": "Explain payment systems in time_series (educational).", "output": "Payment Systems is a foundational concept in econometrics (time series). Core principles include: aggregate demand-supply interactions, discounting future streams, inventory flow balancing. It is often expressed by relations such as Sharpe = (Rp - Rf) / σp and applied in contexts like supply chain inventory optimization."}, "annotations": {"semantic_labels": [{"object": "payment_systems", "category": "time_series", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "payment_systems", "relation": "relates_to_domain", "entity_2": "econometrics"}], "intent_sentiment": {"intent": "educational_summary", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of payment systems.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "medium_term", "stability": "volatile"}, "quality_confidence": {"confidence_score": 0.95, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "econometrics", "subdomain": "time_series", "topic": "payment_systems"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Payment Systems", "description": "Payment Systems explained briefly: Payment Systems is a foundational concept in econometrics (time series). Core principles include: aggregate demand-supply interactions, discounting future streams, inventory flow balancing. It is often expressed by relations such as Sharpe = (Rp - Rf) / σp and applied in contexts like supply chain inventory optimization. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["aggregate demand-supply interactions", "discounting future streams", "inventory flow balancing", "information asymmetry effects", "marginal analysis"], "mathematical_formulas": ["Sharpe = (Rp - Rf) / σp"], "key_applications": ["supply chain inventory optimization", "pricing of derivatives and structured products"], "teaching_examples": ["Example: apply supply-demand shift to simulate tax incidence."], "difficulty_level": "advanced", "estimated_read_time_min": 8, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.597425Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000024", "data_type": "text", "content": {"input": "Explain network effects in time_series (educational).", "output": "Network Effects is a foundational concept in econometrics (time series). Core principles include: marginal analysis, optimization under constraints, information asymmetry effects. It is often expressed by relations such as Leontief Input-Output: x = (I - A)^{-1} f and applied in contexts like behavioral interventions for consumer finance."}, "annotations": {"semantic_labels": [{"object": "network_effects", "category": "time_series", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "network_effects", "relation": "relates_to_domain", "entity_2": "econometrics"}], "intent_sentiment": {"intent": "application_example", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of network effects.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "volatile"}, "quality_confidence": {"confidence_score": 0.96, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "econometrics", "subdomain": "time_series", "topic": "network_effects"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Network Effects", "description": "Network Effects explained briefly: Network Effects is a foundational concept in econometrics (time series). Core principles include: marginal analysis, optimization under constraints, information asymmetry effects. It is often expressed by relations such as Leontief Input-Output: x = (I - A)^{-1} f and applied in contexts like behavioral interventions for consumer finance. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["marginal analysis", "optimization under constraints", "information asymmetry effects", "stochastic shocks and mean reversion", "discounting future streams"], "mathematical_formulas": ["Leontief Input-Output: x = (I - A)^{-1} f"], "key_applications": ["behavioral interventions for consumer finance", "market regulation and compliance"], "teaching_examples": ["Example: compare two portfolios using Sharpe ratio with returns 0.12 and 0.08."], "difficulty_level": "intermediate", "estimated_read_time_min": 6, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.642431Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000025", "data_type": "text", "content": {"input": "Explain yield curve theory in financial_markets (educational).", "output": "Yield Curve Theory is a foundational concept in finance (financial markets). Core principles include: aggregate demand-supply interactions, trade-offs between risk and return, behavioral bias corrections. It is often expressed by relations such as Black-Scholes: C = S N(d1) - K e^{-rT} N(d2) and applied in contexts like credit scoring and loan provisioning."}, "annotations": {"semantic_labels": [{"object": "yield_curve_theory", "category": "financial_markets", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "yield_curve_theory", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "educational_summary", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of yield curve theory.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "medium_term", "stability": "stable"}, "quality_confidence": {"confidence_score": 0.86, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "financial_markets", "topic": "yield_curve_theory"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Yield Curve Theory", "description": "Yield Curve Theory explained briefly: Yield Curve Theory is a foundational concept in finance (financial markets). Core principles include: aggregate demand-supply interactions, trade-offs between risk and return, behavioral bias corrections. It is often expressed by relations such as Black-Scholes: C = S N(d1) - K e^{-rT} N(d2) and applied in contexts like credit scoring and loan provisioning. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["aggregate demand-supply interactions", "trade-offs between risk and return", "behavioral bias corrections", "network externalities and scale effects", "inventory flow balancing"], "mathematical_formulas": ["Black-Scholes: C = S N(d1) - K e^{-rT} N(d2)"], "key_applications": ["credit scoring and loan provisioning", "operational process optimization"], "teaching_examples": ["Example: compute NPV of a three-year project with cashflows [100,200,300]."], "difficulty_level": "advanced", "estimated_read_time_min": 15, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.664754Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000026", "data_type": "text", "content": {"input": "Explain modigliani-miller in behavioral_finance (educational).", "output": "Modigliani-Miller is a foundational concept in behavioral (behavioral finance). Core principles include: opportunity cost reasoning, behavioral bias corrections, optimization under constraints. It is often expressed by relations such as CAPM: E[R] = Rf + β (E[Rm]-Rf) and applied in contexts like credit scoring and loan provisioning."}, "annotations": {"semantic_labels": [{"object": "modigliani-miller", "category": "behavioral_finance", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "modigliani-miller", "relation": "relates_to_domain", "entity_2": "behavioral"}], "intent_sentiment": {"intent": "model_specification", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of modigliani-miller.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.87, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "behavioral", "subdomain": "behavioral_finance", "topic": "modigliani-miller"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Modigliani-Miller", "description": "Modigliani-Miller explained briefly: Modigliani-Miller is a foundational concept in behavioral (behavioral finance). Core principles include: opportunity cost reasoning, behavioral bias corrections, optimization under constraints. It is often expressed by relations such as CAPM: E[R] = Rf + β (E[Rm]-Rf) and applied in contexts like credit scoring and loan provisioning. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["opportunity cost reasoning", "behavioral bias corrections", "optimization under constraints", "agent incentives and moral hazard", "discounting future streams"], "mathematical_formulas": ["CAPM: E[R] = Rf + β (E[Rm]-Rf)"], "key_applications": ["credit scoring and loan provisioning", "asset allocation and portfolio rebalancing"], "teaching_examples": ["Example: compare two portfolios using Sharpe ratio with returns 0.12 and 0.08."], "difficulty_level": "intermediate", "estimated_read_time_min": 15, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.697427Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000027", "data_type": "text", "content": {"input": "Explain comparative advantage in financial_markets (educational).", "output": "Comparative Advantage is a foundational concept in finance (financial markets). Core principles include: market clearing via price adjustments, opportunity cost reasoning, inventory flow balancing. It is often expressed by relations such as Sharpe = (Rp - Rf) / σp and applied in contexts like asset allocation and portfolio rebalancing."}, "annotations": {"semantic_labels": [{"object": "comparative_advantage", "category": "financial_markets", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "comparative_advantage", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "model_specification", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of comparative advantage.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "long_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.88, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "financial_markets", "topic": "comparative_advantage"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Comparative Advantage", "description": "Comparative Advantage explained briefly: Comparative Advantage is a foundational concept in finance (financial markets). Core principles include: market clearing via price adjustments, opportunity cost reasoning, inventory flow balancing. It is often expressed by relations such as Sharpe = (Rp - Rf) / σp and applied in contexts like asset allocation and portfolio rebalancing. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["market clearing via price adjustments", "opportunity cost reasoning", "inventory flow balancing", "stochastic shocks and mean reversion", "network externalities and scale effects"], "mathematical_formulas": ["Sharpe = (Rp - Rf) / σp"], "key_applications": ["asset allocation and portfolio rebalancing", "risk management and hedging strategies"], "teaching_examples": ["Example: compute NPV of a three-year project with cashflows [100,200,300]."], "difficulty_level": "introductory", "estimated_read_time_min": 15, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.741451Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000028", "data_type": "text", "content": {"input": "Explain market equilibrium in operations (educational).", "output": "Market Equilibrium is a foundational concept in business (operations). Core principles include: agent incentives and moral hazard, network externalities and scale effects, aggregate demand-supply interactions. It is often expressed by relations such as IRR: NPV(IRR) = 0 and applied in contexts like supply chain inventory optimization."}, "annotations": {"semantic_labels": [{"object": "market_equilibrium", "category": "operations", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "market_equilibrium", "relation": "relates_to_domain", "entity_2": "business"}], "intent_sentiment": {"intent": "model_specification", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of market equilibrium.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "stable"}, "quality_confidence": {"confidence_score": 0.95, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "business", "subdomain": "operations", "topic": "market_equilibrium"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Market Equilibrium", "description": "Market Equilibrium explained briefly: Market Equilibrium is a foundational concept in business (operations). Core principles include: agent incentives and moral hazard, network externalities and scale effects, aggregate demand-supply interactions. It is often expressed by relations such as IRR: NPV(IRR) = 0 and applied in contexts like supply chain inventory optimization. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["agent incentives and moral hazard", "network externalities and scale effects", "aggregate demand-supply interactions", "optimization under constraints", "behavioral bias corrections"], "mathematical_formulas": ["IRR: NPV(IRR) = 0"], "key_applications": ["supply chain inventory optimization", "market regulation and compliance"], "teaching_examples": ["Example: demonstrate arbitrage between two markets with price gap 0.02."], "difficulty_level": "introductory", "estimated_read_time_min": 17, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.786425Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000029", "data_type": "text", "content": {"input": "Explain market equilibrium in behavioral_finance (educational).", "output": "Market Equilibrium is a foundational concept in behavioral (behavioral finance). Core principles include: information asymmetry effects, aggregate demand-supply interactions, opportunity cost reasoning. It is often expressed by relations such as Sharpe = (Rp - Rf) / σp and applied in contexts like pricing of derivatives and structured products."}, "annotations": {"semantic_labels": [{"object": "market_equilibrium", "category": "behavioral_finance", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "market_equilibrium", "relation": "relates_to_domain", "entity_2": "behavioral"}], "intent_sentiment": {"intent": "technical_explanation", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of market equilibrium.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "long_term", "stability": "stable"}, "quality_confidence": {"confidence_score": 0.95, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "behavioral", "subdomain": "behavioral_finance", "topic": "market_equilibrium"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Market Equilibrium", "description": "Market Equilibrium explained briefly: Market Equilibrium is a foundational concept in behavioral (behavioral finance). Core principles include: information asymmetry effects, aggregate demand-supply interactions, opportunity cost reasoning. It is often expressed by relations such as Sharpe = (Rp - Rf) / σp and applied in contexts like pricing of derivatives and structured products. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["information asymmetry effects", "aggregate demand-supply interactions", "opportunity cost reasoning", "inventory flow balancing", "market clearing via price adjustments"], "mathematical_formulas": ["Sharpe = (Rp - Rf) / σp"], "key_applications": ["pricing of derivatives and structured products", "supply chain inventory optimization"], "teaching_examples": ["Example: apply supply-demand shift to simulate tax incidence."], "difficulty_level": "advanced", "estimated_read_time_min": 8, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.833476Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000030", "data_type": "text", "content": {"input": "Explain liquidity preference in operations (educational).", "output": "Liquidity Preference is a foundational concept in business (operations). Core principles include: opportunity cost reasoning, stochastic shocks and mean reversion, optimization under constraints. It is often expressed by relations such as Black-Scholes: C = S N(d1) - K e^{-rT} N(d2) and applied in contexts like policy design and macro forecasting."}, "annotations": {"semantic_labels": [{"object": "liquidity_preference", "category": "operations", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "liquidity_preference", "relation": "relates_to_domain", "entity_2": "business"}], "intent_sentiment": {"intent": "application_example", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of liquidity preference.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "volatile"}, "quality_confidence": {"confidence_score": 0.97, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "business", "subdomain": "operations", "topic": "liquidity_preference"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Liquidity Preference", "description": "Liquidity Preference explained briefly: Liquidity Preference is a foundational concept in business (operations). Core principles include: opportunity cost reasoning, stochastic shocks and mean reversion, optimization under constraints. It is often expressed by relations such as Black-Scholes: C = S N(d1) - K e^{-rT} N(d2) and applied in contexts like policy design and macro forecasting. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["opportunity cost reasoning", "stochastic shocks and mean reversion", "optimization under constraints", "marginal analysis", "behavioral bias corrections"], "mathematical_formulas": ["Black-Scholes: C = S N(d1) - K e^{-rT} N(d2)"], "key_applications": ["policy design and macro forecasting", "valuation of firms and projects"], "teaching_examples": ["Example: demonstrate arbitrage between two markets with price gap 0.02."], "difficulty_level": "introductory", "estimated_read_time_min": 17, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.881007Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000031", "data_type": "text", "content": {"input": "Explain pecking order theory in financial_markets (educational).", "output": "Pecking Order Theory is a foundational concept in finance (financial markets). Core principles include: trade-offs between risk and return, network externalities and scale effects, inventory flow balancing. It is often expressed by relations such as CAPM: E[R] = Rf + β (E[Rm]-Rf) and applied in contexts like policy design and macro forecasting."}, "annotations": {"semantic_labels": [{"object": "pecking_order_theory", "category": "financial_markets", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "pecking_order_theory", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "application_example", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of pecking order theory.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "long_term", "stability": "volatile"}, "quality_confidence": {"confidence_score": 0.98, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "financial_markets", "topic": "pecking_order_theory"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Pecking Order Theory", "description": "Pecking Order Theory explained briefly: Pecking Order Theory is a foundational concept in finance (financial markets). Core principles include: trade-offs between risk and return, network externalities and scale effects, inventory flow balancing. It is often expressed by relations such as CAPM: E[R] = Rf + β (E[Rm]-Rf) and applied in contexts like policy design and macro forecasting. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["trade-offs between risk and return", "network externalities and scale effects", "inventory flow balancing", "discounting future streams", "aggregate demand-supply interactions"], "mathematical_formulas": ["CAPM: E[R] = Rf + β (E[Rm]-Rf)"], "key_applications": ["policy design and macro forecasting", "credit scoring and loan provisioning"], "teaching_examples": ["Example: stress-test a bank's portfolio under 30% market drop."], "difficulty_level": "introductory", "estimated_read_time_min": 11, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.924797Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000032", "data_type": "text", "content": {"input": "Explain cost-benefit analysis in corporate_finance (educational).", "output": "Cost-Benefit Analysis is a foundational concept in finance (corporate finance). Core principles include: opportunity cost reasoning, discounting future streams, information asymmetry effects. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like risk management and hedging strategies."}, "annotations": {"semantic_labels": [{"object": "cost-benefit_analysis", "category": "corporate_finance", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "cost-benefit_analysis", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "educational_summary", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of cost-benefit analysis.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "long_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.87, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "corporate_finance", "topic": "cost-benefit_analysis"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Cost-Benefit Analysis", "description": "Cost-Benefit Analysis explained briefly: Cost-Benefit Analysis is a foundational concept in finance (corporate finance). Core principles include: opportunity cost reasoning, discounting future streams, information asymmetry effects. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like risk management and hedging strategies. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["opportunity cost reasoning", "discounting future streams", "information asymmetry effects", "aggregate demand-supply interactions", "stochastic shocks and mean reversion"], "mathematical_formulas": ["DebtRatio = Debt / (Debt + Equity)"], "key_applications": ["risk management and hedging strategies", "policy design and macro forecasting"], "teaching_examples": ["Example: compute NPV of a three-year project with cashflows [100,200,300]."], "difficulty_level": "intermediate", "estimated_read_time_min": 17, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:00.977854Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000033", "data_type": "text", "content": {"input": "Explain cost-benefit analysis in macroeconomics (educational).", "output": "Cost-Benefit Analysis is a foundational concept in economics (macroeconomics). Core principles include: agent incentives and moral hazard, marginal analysis, optimization under constraints. It is often expressed by relations such as Black-Scholes: C = S N(d1) - K e^{-rT} N(d2) and applied in contexts like pricing of derivatives and structured products."}, "annotations": {"semantic_labels": [{"object": "cost-benefit_analysis", "category": "macroeconomics", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "cost-benefit_analysis", "relation": "relates_to_domain", "entity_2": "economics"}], "intent_sentiment": {"intent": "technical_explanation", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of cost-benefit analysis.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.93, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "economics", "subdomain": "macroeconomics", "topic": "cost-benefit_analysis"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Cost-Benefit Analysis", "description": "Cost-Benefit Analysis explained briefly: Cost-Benefit Analysis is a foundational concept in economics (macroeconomics). Core principles include: agent incentives and moral hazard, marginal analysis, optimization under constraints. It is often expressed by relations such as Black-Scholes: C = S N(d1) - K e^{-rT} N(d2) and applied in contexts like pricing of derivatives and structured products. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["agent incentives and moral hazard", "marginal analysis", "optimization under constraints", "inventory flow balancing", "network externalities and scale effects"], "mathematical_formulas": ["Black-Scholes: C = S N(d1) - K e^{-rT} N(d2)"], "key_applications": ["pricing of derivatives and structured products", "credit scoring and loan provisioning"], "teaching_examples": ["Example: compute NPV of a three-year project with cashflows [100,200,300]."], "difficulty_level": "introductory", "estimated_read_time_min": 15, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:01.024968Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000034", "data_type": "text", "content": {"input": "Explain supply-demand model in microeconomics (educational).", "output": "Supply-Demand Model is a foundational concept in economics (microeconomics). Core principles include: trade-offs between risk and return, market clearing via price adjustments, aggregate demand-supply interactions. It is often expressed by relations such as Black-Scholes: C = S N(d1) - K e^{-rT} N(d2) and applied in contexts like supply chain inventory optimization."}, "annotations": {"semantic_labels": [{"object": "supply-demand_model", "category": "microeconomics", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "supply-demand_model", "relation": "relates_to_domain", "entity_2": "economics"}], "intent_sentiment": {"intent": "technical_explanation", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of supply-demand model.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "long_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.94, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "economics", "subdomain": "microeconomics", "topic": "supply-demand_model"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Supply-Demand Model", "description": "Supply-Demand Model explained briefly: Supply-Demand Model is a foundational concept in economics (microeconomics). Core principles include: trade-offs between risk and return, market clearing via price adjustments, aggregate demand-supply interactions. It is often expressed by relations such as Black-Scholes: C = S N(d1) - K e^{-rT} N(d2) and applied in contexts like supply chain inventory optimization. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["trade-offs between risk and return", "market clearing via price adjustments", "aggregate demand-supply interactions", "information asymmetry effects", "behavioral bias corrections"], "mathematical_formulas": ["Black-Scholes: C = S N(d1) - K e^{-rT} N(d2)"], "key_applications": ["supply chain inventory optimization", "policy design and macro forecasting"], "teaching_examples": ["Example: compute NPV of a three-year project with cashflows [100,200,300]."], "difficulty_level": "intermediate", "estimated_read_time_min": 4, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:01.064218Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000035", "data_type": "text", "content": {"input": "Explain payment systems in financial_markets (educational).", "output": "Payment Systems is a foundational concept in finance (financial markets). Core principles include: aggregate demand-supply interactions, market clearing via price adjustments, stochastic shocks and mean reversion. It is often expressed by relations such as Leontief Input-Output: x = (I - A)^{-1} f and applied in contexts like asset allocation and portfolio rebalancing."}, "annotations": {"semantic_labels": [{"object": "payment_systems", "category": "financial_markets", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "payment_systems", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "model_specification", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of payment systems.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "long_term", "stability": "volatile"}, "quality_confidence": {"confidence_score": 0.97, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "financial_markets", "topic": "payment_systems"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Payment Systems", "description": "Payment Systems explained briefly: Payment Systems is a foundational concept in finance (financial markets). Core principles include: aggregate demand-supply interactions, market clearing via price adjustments, stochastic shocks and mean reversion. It is often expressed by relations such as Leontief Input-Output: x = (I - A)^{-1} f and applied in contexts like asset allocation and portfolio rebalancing. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["aggregate demand-supply interactions", "market clearing via price adjustments", "stochastic shocks and mean reversion", "opportunity cost reasoning", "inventory flow balancing"], "mathematical_formulas": ["Leontief Input-Output: x = (I - A)^{-1} f"], "key_applications": ["asset allocation and portfolio rebalancing", "supply chain inventory optimization"], "teaching_examples": ["Example: compute NPV of a three-year project with cashflows [100,200,300]."], "difficulty_level": "advanced", "estimated_read_time_min": 10, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:01.135962Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000036", "data_type": "text", "content": {"input": "Explain market equilibrium in macroeconomics (educational).", "output": "Market Equilibrium is a foundational concept in economics (macroeconomics). Core principles include: stochastic shocks and mean reversion, network externalities and scale effects, trade-offs between risk and return. It is often expressed by relations such as Leontief Input-Output: x = (I - A)^{-1} f and applied in contexts like risk management and hedging strategies."}, "annotations": {"semantic_labels": [{"object": "market_equilibrium", "category": "macroeconomics", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "market_equilibrium", "relation": "relates_to_domain", "entity_2": "economics"}], "intent_sentiment": {"intent": "model_specification", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of market equilibrium.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "long_term", "stability": "volatile"}, "quality_confidence": {"confidence_score": 0.88, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "economics", "subdomain": "macroeconomics", "topic": "market_equilibrium"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Market Equilibrium", "description": "Market Equilibrium explained briefly: Market Equilibrium is a foundational concept in economics (macroeconomics). Core principles include: stochastic shocks and mean reversion, network externalities and scale effects, trade-offs between risk and return. It is often expressed by relations such as Leontief Input-Output: x = (I - A)^{-1} f and applied in contexts like risk management and hedging strategies. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["stochastic shocks and mean reversion", "network externalities and scale effects", "trade-offs between risk and return", "optimization under constraints", "aggregate demand-supply interactions"], "mathematical_formulas": ["Leontief Input-Output: x = (I - A)^{-1} f"], "key_applications": ["risk management and hedging strategies", "supply chain inventory optimization"], "teaching_examples": ["Example: stress-test a bank's portfolio under 30% market drop."], "difficulty_level": "introductory", "estimated_read_time_min": 19, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:01.185355Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000037", "data_type": "text", "content": {"input": "Explain lean operations in microeconomics (educational).", "output": "Lean Operations is a foundational concept in economics (microeconomics). Core principles include: discounting future streams, network externalities and scale effects, trade-offs between risk and return. It is often expressed by relations such as CAPM: E[R] = Rf + β (E[Rm]-Rf) and applied in contexts like policy design and macro forecasting."}, "annotations": {"semantic_labels": [{"object": "lean_operations", "category": "microeconomics", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "lean_operations", "relation": "relates_to_domain", "entity_2": "economics"}], "intent_sentiment": {"intent": "model_specification", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of lean operations.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.95, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "economics", "subdomain": "microeconomics", "topic": "lean_operations"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Lean Operations", "description": "Lean Operations explained briefly: Lean Operations is a foundational concept in economics (microeconomics). Core principles include: discounting future streams, network externalities and scale effects, trade-offs between risk and return. It is often expressed by relations such as CAPM: E[R] = Rf + β (E[Rm]-Rf) and applied in contexts like policy design and macro forecasting. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["discounting future streams", "network externalities and scale effects", "trade-offs between risk and return", "agent incentives and moral hazard", "market clearing via price adjustments"], "mathematical_formulas": ["CAPM: E[R] = Rf + β (E[Rm]-Rf)"], "key_applications": ["policy design and macro forecasting", "pricing of derivatives and structured products"], "teaching_examples": ["Example: apply supply-demand shift to simulate tax incidence."], "difficulty_level": "introductory", "estimated_read_time_min": 4, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:01.234782Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000038", "data_type": "text", "content": {"input": "Explain monetary policy transmission in investments (educational).", "output": "Monetary Policy Transmission is a foundational concept in finance (investments). Core principles include: market clearing via price adjustments, agent incentives and moral hazard, discounting future streams. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like asset allocation and portfolio rebalancing."}, "annotations": {"semantic_labels": [{"object": "monetary_policy_transmission", "category": "investments", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "monetary_policy_transmission", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "application_example", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of monetary policy transmission.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "volatile"}, "quality_confidence": {"confidence_score": 0.93, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "investments", "topic": "monetary_policy_transmission"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Monetary Policy Transmission", "description": "Monetary Policy Transmission explained briefly: Monetary Policy Transmission is a foundational concept in finance (investments). Core principles include: market clearing via price adjustments, agent incentives and moral hazard, discounting future streams. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like asset allocation and portfolio rebalancing. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["market clearing via price adjustments", "agent incentives and moral hazard", "discounting future streams", "inventory flow balancing", "information asymmetry effects"], "mathematical_formulas": ["DebtRatio = Debt / (Debt + Equity)"], "key_applications": ["asset allocation and portfolio rebalancing", "behavioral interventions for consumer finance"], "teaching_examples": ["Example: stress-test a bank's portfolio under 30% market drop."], "difficulty_level": "advanced", "estimated_read_time_min": 11, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:01.281473Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000039", "data_type": "text", "content": {"input": "Explain lean operations in financial_accounting (educational).", "output": "Lean Operations is a foundational concept in accounting (financial accounting). Core principles include: inventory flow balancing, information asymmetry effects, network externalities and scale effects. It is often expressed by relations such as Sharpe = (Rp - Rf) / σp and applied in contexts like risk management and hedging strategies."}, "annotations": {"semantic_labels": [{"object": "lean_operations", "category": "financial_accounting", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "lean_operations", "relation": "relates_to_domain", "entity_2": "accounting"}], "intent_sentiment": {"intent": "application_example", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of lean operations.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "long_term", "stability": "stable"}, "quality_confidence": {"confidence_score": 0.9, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "accounting", "subdomain": "financial_accounting", "topic": "lean_operations"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Lean Operations", "description": "Lean Operations explained briefly: Lean Operations is a foundational concept in accounting (financial accounting). Core principles include: inventory flow balancing, information asymmetry effects, network externalities and scale effects. It is often expressed by relations such as Sharpe = (Rp - Rf) / σp and applied in contexts like risk management and hedging strategies. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["inventory flow balancing", "information asymmetry effects", "network externalities and scale effects", "behavioral bias corrections", "aggregate demand-supply interactions"], "mathematical_formulas": ["Sharpe = (Rp - Rf) / σp"], "key_applications": ["risk management and hedging strategies", "market regulation and compliance"], "teaching_examples": ["Example: compare two portfolios using Sharpe ratio with returns 0.12 and 0.08."], "difficulty_level": "introductory", "estimated_read_time_min": 19, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:01.326756Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000040", "data_type": "text", "content": {"input": "Explain agency theory in financial_markets (educational).", "output": "Agency Theory is a foundational concept in finance (financial markets). Core principles include: opportunity cost reasoning, market clearing via price adjustments, aggregate demand-supply interactions. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like market regulation and compliance."}, "annotations": {"semantic_labels": [{"object": "agency_theory", "category": "financial_markets", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "agency_theory", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "technical_explanation", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of agency theory.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "medium_term", "stability": "volatile"}, "quality_confidence": {"confidence_score": 0.94, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "financial_markets", "topic": "agency_theory"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Agency Theory", "description": "Agency Theory explained briefly: Agency Theory is a foundational concept in finance (financial markets). Core principles include: opportunity cost reasoning, market clearing via price adjustments, aggregate demand-supply interactions. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like market regulation and compliance. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["opportunity cost reasoning", "market clearing via price adjustments", "aggregate demand-supply interactions", "trade-offs between risk and return", "optimization under constraints"], "mathematical_formulas": ["DebtRatio = Debt / (Debt + Equity)"], "key_applications": ["market regulation and compliance", "policy design and macro forecasting"], "teaching_examples": ["Example: apply supply-demand shift to simulate tax incidence."], "difficulty_level": "introductory", "estimated_read_time_min": 15, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:01.371958Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000041", "data_type": "text", "content": {"input": "Explain stress testing in financial_markets (educational).", "output": "Stress Testing is a foundational concept in finance (financial markets). Core principles include: stochastic shocks and mean reversion, aggregate demand-supply interactions, network externalities and scale effects. It is often expressed by relations such as CAPM: E[R] = Rf + β (E[Rm]-Rf) and applied in contexts like operational process optimization."}, "annotations": {"semantic_labels": [{"object": "stress_testing", "category": "financial_markets", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "stress_testing", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "educational_summary", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of stress testing.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "medium_term", "stability": "stable"}, "quality_confidence": {"confidence_score": 0.94, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "financial_markets", "topic": "stress_testing"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Stress Testing", "description": "Stress Testing explained briefly: Stress Testing is a foundational concept in finance (financial markets). Core principles include: stochastic shocks and mean reversion, aggregate demand-supply interactions, network externalities and scale effects. It is often expressed by relations such as CAPM: E[R] = Rf + β (E[Rm]-Rf) and applied in contexts like operational process optimization. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["stochastic shocks and mean reversion", "aggregate demand-supply interactions", "network externalities and scale effects", "optimization under constraints", "information asymmetry effects"], "mathematical_formulas": ["CAPM: E[R] = Rf + β (E[Rm]-Rf)"], "key_applications": ["operational process optimization", "asset allocation and portfolio rebalancing"], "teaching_examples": ["Example: compare two portfolios using Sharpe ratio with returns 0.12 and 0.08."], "difficulty_level": "introductory", "estimated_read_time_min": 14, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:01.419368Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000042", "data_type": "text", "content": {"input": "Explain stress testing in strategy (educational).", "output": "Stress Testing is a foundational concept in business (strategy). Core principles include: inventory flow balancing, stochastic shocks and mean reversion, trade-offs between risk and return. It is often expressed by relations such as NPV = Σ (Ct / (1 + r)^t) - C0 and applied in contexts like market regulation and compliance."}, "annotations": {"semantic_labels": [{"object": "stress_testing", "category": "strategy", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "stress_testing", "relation": "relates_to_domain", "entity_2": "business"}], "intent_sentiment": {"intent": "model_specification", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of stress testing.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "medium_term", "stability": "volatile"}, "quality_confidence": {"confidence_score": 0.94, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "business", "subdomain": "strategy", "topic": "stress_testing"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Stress Testing", "description": "Stress Testing explained briefly: Stress Testing is a foundational concept in business (strategy). Core principles include: inventory flow balancing, stochastic shocks and mean reversion, trade-offs between risk and return. It is often expressed by relations such as NPV = Σ (Ct / (1 + r)^t) - C0 and applied in contexts like market regulation and compliance. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["inventory flow balancing", "stochastic shocks and mean reversion", "trade-offs between risk and return", "agent incentives and moral hazard", "marginal analysis"], "mathematical_formulas": ["NPV = Σ (Ct / (1 + r)^t) - C0"], "key_applications": ["market regulation and compliance", "supply chain inventory optimization"], "teaching_examples": ["Example: compare two portfolios using Sharpe ratio with returns 0.12 and 0.08."], "difficulty_level": "advanced", "estimated_read_time_min": 8, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:01.461997Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000043", "data_type": "text", "content": {"input": "Explain network effects in financial_markets (educational).", "output": "Network Effects is a foundational concept in finance (financial markets). Core principles include: trade-offs between risk and return, agent incentives and moral hazard, network externalities and scale effects. It is often expressed by relations such as Sharpe = (Rp - Rf) / σp and applied in contexts like behavioral interventions for consumer finance."}, "annotations": {"semantic_labels": [{"object": "network_effects", "category": "financial_markets", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "network_effects", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "model_specification", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of network effects.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.88, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "financial_markets", "topic": "network_effects"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Network Effects", "description": "Network Effects explained briefly: Network Effects is a foundational concept in finance (financial markets). Core principles include: trade-offs between risk and return, agent incentives and moral hazard, network externalities and scale effects. It is often expressed by relations such as Sharpe = (Rp - Rf) / σp and applied in contexts like behavioral interventions for consumer finance. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["trade-offs between risk and return", "agent incentives and moral hazard", "network externalities and scale effects", "aggregate demand-supply interactions", "inventory flow balancing"], "mathematical_formulas": ["Sharpe = (Rp - Rf) / σp"], "key_applications": ["behavioral interventions for consumer finance", "operational process optimization"], "teaching_examples": ["Example: compute NPV of a three-year project with cashflows [100,200,300]."], "difficulty_level": "introductory", "estimated_read_time_min": 11, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:01.510728Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000044", "data_type": "text", "content": {"input": "Explain arbitrage pricing in strategy (educational).", "output": "Arbitrage Pricing is a foundational concept in business (strategy). Core principles include: agent incentives and moral hazard, discounting future streams, information asymmetry effects. It is often expressed by relations such as IRR: NPV(IRR) = 0 and applied in contexts like risk management and hedging strategies."}, "annotations": {"semantic_labels": [{"object": "arbitrage_pricing", "category": "strategy", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "arbitrage_pricing", "relation": "relates_to_domain", "entity_2": "business"}], "intent_sentiment": {"intent": "technical_explanation", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of arbitrage pricing.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "long_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.96, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "business", "subdomain": "strategy", "topic": "arbitrage_pricing"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Arbitrage Pricing", "description": "Arbitrage Pricing explained briefly: Arbitrage Pricing is a foundational concept in business (strategy). Core principles include: agent incentives and moral hazard, discounting future streams, information asymmetry effects. It is often expressed by relations such as IRR: NPV(IRR) = 0 and applied in contexts like risk management and hedging strategies. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["agent incentives and moral hazard", "discounting future streams", "information asymmetry effects", "behavioral bias corrections", "market clearing via price adjustments"], "mathematical_formulas": ["IRR: NPV(IRR) = 0"], "key_applications": ["risk management and hedging strategies", "supply chain inventory optimization"], "teaching_examples": ["Example: compare two portfolios using Sharpe ratio with returns 0.12 and 0.08."], "difficulty_level": "introductory", "estimated_read_time_min": 10, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:01.561747Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000045", "data_type": "text", "content": {"input": "Explain currency carry trade in financial_markets (educational).", "output": "Currency Carry Trade is a foundational concept in finance (financial markets). Core principles include: stochastic shocks and mean reversion, trade-offs between risk and return, network externalities and scale effects. It is often expressed by relations such as CAPM: E[R] = Rf + β (E[Rm]-Rf) and applied in contexts like asset allocation and portfolio rebalancing."}, "annotations": {"semantic_labels": [{"object": "currency_carry_trade", "category": "financial_markets", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "currency_carry_trade", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "model_specification", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of currency carry trade.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "stable"}, "quality_confidence": {"confidence_score": 0.92, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "financial_markets", "topic": "currency_carry_trade"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Currency Carry Trade", "description": "Currency Carry Trade explained briefly: Currency Carry Trade is a foundational concept in finance (financial markets). Core principles include: stochastic shocks and mean reversion, trade-offs between risk and return, network externalities and scale effects. It is often expressed by relations such as CAPM: E[R] = Rf + β (E[Rm]-Rf) and applied in contexts like asset allocation and portfolio rebalancing. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["stochastic shocks and mean reversion", "trade-offs between risk and return", "network externalities and scale effects", "information asymmetry effects", "behavioral bias corrections"], "mathematical_formulas": ["CAPM: E[R] = Rf + β (E[Rm]-Rf)"], "key_applications": ["asset allocation and portfolio rebalancing", "supply chain inventory optimization"], "teaching_examples": ["Example: demonstrate arbitrage between two markets with price gap 0.02."], "difficulty_level": "intermediate", "estimated_read_time_min": 10, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:01.604275Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000046", "data_type": "text", "content": {"input": "Explain option pricing in behavioral_finance (educational).", "output": "Option Pricing is a foundational concept in behavioral (behavioral finance). Core principles include: agent incentives and moral hazard, stochastic shocks and mean reversion, optimization under constraints. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like market regulation and compliance."}, "annotations": {"semantic_labels": [{"object": "option_pricing", "category": "behavioral_finance", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "option_pricing", "relation": "relates_to_domain", "entity_2": "behavioral"}], "intent_sentiment": {"intent": "technical_explanation", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of option pricing.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "long_term", "stability": "stable"}, "quality_confidence": {"confidence_score": 0.89, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "behavioral", "subdomain": "behavioral_finance", "topic": "option_pricing"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Option Pricing", "description": "Option Pricing explained briefly: Option Pricing is a foundational concept in behavioral (behavioral finance). Core principles include: agent incentives and moral hazard, stochastic shocks and mean reversion, optimization under constraints. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like market regulation and compliance. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["agent incentives and moral hazard", "stochastic shocks and mean reversion", "optimization under constraints", "network externalities and scale effects", "behavioral bias corrections"], "mathematical_formulas": ["DebtRatio = Debt / (Debt + Equity)"], "key_applications": ["market regulation and compliance", "valuation of firms and projects"], "teaching_examples": ["Example: stress-test a bank's portfolio under 30% market drop."], "difficulty_level": "advanced", "estimated_read_time_min": 11, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:01.653103Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000047", "data_type": "text", "content": {"input": "Explain stress testing in strategy (educational).", "output": "Stress Testing is a foundational concept in business (strategy). Core principles include: aggregate demand-supply interactions, opportunity cost reasoning, trade-offs between risk and return. It is often expressed by relations such as Leontief Input-Output: x = (I - A)^{-1} f and applied in contexts like behavioral interventions for consumer finance."}, "annotations": {"semantic_labels": [{"object": "stress_testing", "category": "strategy", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "stress_testing", "relation": "relates_to_domain", "entity_2": "business"}], "intent_sentiment": {"intent": "technical_explanation", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of stress testing.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "volatile"}, "quality_confidence": {"confidence_score": 0.96, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "business", "subdomain": "strategy", "topic": "stress_testing"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Stress Testing", "description": "Stress Testing explained briefly: Stress Testing is a foundational concept in business (strategy). Core principles include: aggregate demand-supply interactions, opportunity cost reasoning, trade-offs between risk and return. It is often expressed by relations such as Leontief Input-Output: x = (I - A)^{-1} f and applied in contexts like behavioral interventions for consumer finance. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["aggregate demand-supply interactions", "opportunity cost reasoning", "trade-offs between risk and return", "market clearing via price adjustments", "stochastic shocks and mean reversion"], "mathematical_formulas": ["Leontief Input-Output: x = (I - A)^{-1} f"], "key_applications": ["behavioral interventions for consumer finance", "policy design and macro forecasting"], "teaching_examples": ["Example: apply supply-demand shift to simulate tax incidence."], "difficulty_level": "introductory", "estimated_read_time_min": 7, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:01.701369Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000048", "data_type": "text", "content": {"input": "Explain game theory in financial_markets (educational).", "output": "Game Theory is a foundational concept in finance (financial markets). Core principles include: trade-offs between risk and return, agent incentives and moral hazard, opportunity cost reasoning. It is often expressed by relations such as NPV = Σ (Ct / (1 + r)^t) - C0 and applied in contexts like risk management and hedging strategies."}, "annotations": {"semantic_labels": [{"object": "game_theory", "category": "financial_markets", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "game_theory", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "model_specification", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of game theory.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "medium_term", "stability": "stable"}, "quality_confidence": {"confidence_score": 0.86, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "financial_markets", "topic": "game_theory"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Game Theory", "description": "Game Theory explained briefly: Game Theory is a foundational concept in finance (financial markets). Core principles include: trade-offs between risk and return, agent incentives and moral hazard, opportunity cost reasoning. It is often expressed by relations such as NPV = Σ (Ct / (1 + r)^t) - C0 and applied in contexts like risk management and hedging strategies. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["trade-offs between risk and return", "agent incentives and moral hazard", "opportunity cost reasoning", "network externalities and scale effects", "behavioral bias corrections"], "mathematical_formulas": ["NPV = Σ (Ct / (1 + r)^t) - C0"], "key_applications": ["risk management and hedging strategies", "asset allocation and portfolio rebalancing"], "teaching_examples": ["Example: compute NPV of a three-year project with cashflows [100,200,300]."], "difficulty_level": "introductory", "estimated_read_time_min": 16, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:01.750045Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000049", "data_type": "text", "content": {"input": "Explain payment systems in financial_accounting (educational).", "output": "Payment Systems is a foundational concept in accounting (financial accounting). Core principles include: agent incentives and moral hazard, behavioral bias corrections, network externalities and scale effects. It is often expressed by relations such as IRR: NPV(IRR) = 0 and applied in contexts like credit scoring and loan provisioning."}, "annotations": {"semantic_labels": [{"object": "payment_systems", "category": "financial_accounting", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "payment_systems", "relation": "relates_to_domain", "entity_2": "accounting"}], "intent_sentiment": {"intent": "application_example", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of payment systems.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "medium_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.89, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "accounting", "subdomain": "financial_accounting", "topic": "payment_systems"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Payment Systems", "description": "Payment Systems explained briefly: Payment Systems is a foundational concept in accounting (financial accounting). Core principles include: agent incentives and moral hazard, behavioral bias corrections, network externalities and scale effects. It is often expressed by relations such as IRR: NPV(IRR) = 0 and applied in contexts like credit scoring and loan provisioning. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["agent incentives and moral hazard", "behavioral bias corrections", "network externalities and scale effects", "aggregate demand-supply interactions", "information asymmetry effects"], "mathematical_formulas": ["IRR: NPV(IRR) = 0"], "key_applications": ["credit scoring and loan provisioning", "market regulation and compliance"], "teaching_examples": ["Example: apply supply-demand shift to simulate tax incidence."], "difficulty_level": "introductory", "estimated_read_time_min": 7, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:01.801885Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000050", "data_type": "text", "content": {"input": "Explain pecking order theory in financial_markets (educational).", "output": "Pecking Order Theory is a foundational concept in finance (financial markets). Core principles include: network externalities and scale effects, trade-offs between risk and return, inventory flow balancing. It is often expressed by relations such as IRR: NPV(IRR) = 0 and applied in contexts like supply chain inventory optimization."}, "annotations": {"semantic_labels": [{"object": "pecking_order_theory", "category": "financial_markets", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "pecking_order_theory", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "technical_explanation", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of pecking order theory.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "long_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.91, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "financial_markets", "topic": "pecking_order_theory"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Pecking Order Theory", "description": "Pecking Order Theory explained briefly: Pecking Order Theory is a foundational concept in finance (financial markets). Core principles include: network externalities and scale effects, trade-offs between risk and return, inventory flow balancing. It is often expressed by relations such as IRR: NPV(IRR) = 0 and applied in contexts like supply chain inventory optimization. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["network externalities and scale effects", "trade-offs between risk and return", "inventory flow balancing", "opportunity cost reasoning", "optimization under constraints"], "mathematical_formulas": ["IRR: NPV(IRR) = 0"], "key_applications": ["supply chain inventory optimization", "market regulation and compliance"], "teaching_examples": ["Example: compute NPV of a three-year project with cashflows [100,200,300]."], "difficulty_level": "intermediate", "estimated_read_time_min": 16, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:01.848206Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000051", "data_type": "text", "content": {"input": "Explain yield curve theory in microeconomics (educational).", "output": "Yield Curve Theory is a foundational concept in economics (microeconomics). Core principles include: marginal analysis, agent incentives and moral hazard, behavioral bias corrections. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like risk management and hedging strategies."}, "annotations": {"semantic_labels": [{"object": "yield_curve_theory", "category": "microeconomics", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "yield_curve_theory", "relation": "relates_to_domain", "entity_2": "economics"}], "intent_sentiment": {"intent": "application_example", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of yield curve theory.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "medium_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.96, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "economics", "subdomain": "microeconomics", "topic": "yield_curve_theory"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Yield Curve Theory", "description": "Yield Curve Theory explained briefly: Yield Curve Theory is a foundational concept in economics (microeconomics). Core principles include: marginal analysis, agent incentives and moral hazard, behavioral bias corrections. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like risk management and hedging strategies. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["marginal analysis", "agent incentives and moral hazard", "behavioral bias corrections", "information asymmetry effects", "market clearing via price adjustments"], "mathematical_formulas": ["DebtRatio = Debt / (Debt + Equity)"], "key_applications": ["risk management and hedging strategies", "valuation of firms and projects"], "teaching_examples": ["Example: apply supply-demand shift to simulate tax incidence."], "difficulty_level": "advanced", "estimated_read_time_min": 14, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:01.898420Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000052", "data_type": "text", "content": {"input": "Explain agency theory in investments (educational).", "output": "Agency Theory is a foundational concept in finance (investments). Core principles include: stochastic shocks and mean reversion, optimization under constraints, behavioral bias corrections. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like supply chain inventory optimization."}, "annotations": {"semantic_labels": [{"object": "agency_theory", "category": "investments", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "agency_theory", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "educational_summary", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of agency theory.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.86, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "investments", "topic": "agency_theory"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Agency Theory", "description": "Agency Theory explained briefly: Agency Theory is a foundational concept in finance (investments). Core principles include: stochastic shocks and mean reversion, optimization under constraints, behavioral bias corrections. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like supply chain inventory optimization. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["stochastic shocks and mean reversion", "optimization under constraints", "behavioral bias corrections", "network externalities and scale effects", "aggregate demand-supply interactions"], "mathematical_formulas": ["DebtRatio = Debt / (Debt + Equity)"], "key_applications": ["supply chain inventory optimization", "pricing of derivatives and structured products"], "teaching_examples": ["Example: stress-test a bank's portfolio under 30% market drop."], "difficulty_level": "intermediate", "estimated_read_time_min": 7, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:01.949317Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000053", "data_type": "text", "content": {"input": "Explain agency theory in investments (educational).", "output": "Agency Theory is a foundational concept in finance (investments). Core principles include: stochastic shocks and mean reversion, market clearing via price adjustments, behavioral bias corrections. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like behavioral interventions for consumer finance."}, "annotations": {"semantic_labels": [{"object": "agency_theory", "category": "investments", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "agency_theory", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "technical_explanation", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of agency theory.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "stable"}, "quality_confidence": {"confidence_score": 0.97, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "investments", "topic": "agency_theory"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Agency Theory", "description": "Agency Theory explained briefly: Agency Theory is a foundational concept in finance (investments). Core principles include: stochastic shocks and mean reversion, market clearing via price adjustments, behavioral bias corrections. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like behavioral interventions for consumer finance. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["stochastic shocks and mean reversion", "market clearing via price adjustments", "behavioral bias corrections", "opportunity cost reasoning", "agent incentives and moral hazard"], "mathematical_formulas": ["DebtRatio = Debt / (Debt + Equity)"], "key_applications": ["behavioral interventions for consumer finance", "risk management and hedging strategies"], "teaching_examples": ["Example: compute NPV of a three-year project with cashflows [100,200,300]."], "difficulty_level": "introductory", "estimated_read_time_min": 11, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:02.000403Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000054", "data_type": "text", "content": {"input": "Explain agency theory in corporate_finance (educational).", "output": "Agency Theory is a foundational concept in finance (corporate finance). Core principles include: opportunity cost reasoning, agent incentives and moral hazard, behavioral bias corrections. It is often expressed by relations such as NPV = Σ (Ct / (1 + r)^t) - C0 and applied in contexts like policy design and macro forecasting."}, "annotations": {"semantic_labels": [{"object": "agency_theory", "category": "corporate_finance", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "agency_theory", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "technical_explanation", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of agency theory.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "medium_term", "stability": "volatile"}, "quality_confidence": {"confidence_score": 0.95, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "corporate_finance", "topic": "agency_theory"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Agency Theory", "description": "Agency Theory explained briefly: Agency Theory is a foundational concept in finance (corporate finance). Core principles include: opportunity cost reasoning, agent incentives and moral hazard, behavioral bias corrections. It is often expressed by relations such as NPV = Σ (Ct / (1 + r)^t) - C0 and applied in contexts like policy design and macro forecasting. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["opportunity cost reasoning", "agent incentives and moral hazard", "behavioral bias corrections", "aggregate demand-supply interactions", "network externalities and scale effects"], "mathematical_formulas": ["NPV = Σ (Ct / (1 + r)^t) - C0"], "key_applications": ["policy design and macro forecasting", "pricing of derivatives and structured products"], "teaching_examples": ["Example: compute NPV of a three-year project with cashflows [100,200,300]."], "difficulty_level": "introductory", "estimated_read_time_min": 12, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:02.042063Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000055", "data_type": "text", "content": {"input": "Explain lean operations in investments (educational).", "output": "Lean Operations is a foundational concept in finance (investments). Core principles include: marginal analysis, trade-offs between risk and return, discounting future streams. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like valuation of firms and projects."}, "annotations": {"semantic_labels": [{"object": "lean_operations", "category": "investments", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "lean_operations", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "application_example", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of lean operations.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "volatile"}, "quality_confidence": {"confidence_score": 0.93, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "investments", "topic": "lean_operations"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Lean Operations", "description": "Lean Operations explained briefly: Lean Operations is a foundational concept in finance (investments). Core principles include: marginal analysis, trade-offs between risk and return, discounting future streams. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like valuation of firms and projects. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["marginal analysis", "trade-offs between risk and return", "discounting future streams", "information asymmetry effects", "agent incentives and moral hazard"], "mathematical_formulas": ["DebtRatio = Debt / (Debt + Equity)"], "key_applications": ["valuation of firms and projects", "behavioral interventions for consumer finance"], "teaching_examples": ["Example: compare two portfolios using Sharpe ratio with returns 0.12 and 0.08."], "difficulty_level": "introductory", "estimated_read_time_min": 9, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:02.080042Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000056", "data_type": "text", "content": {"input": "Explain game theory in corporate_finance (educational).", "output": "Game Theory is a foundational concept in finance (corporate finance). Core principles include: market clearing via price adjustments, stochastic shocks and mean reversion, agent incentives and moral hazard. It is often expressed by relations such as IRR: NPV(IRR) = 0 and applied in contexts like valuation of firms and projects."}, "annotations": {"semantic_labels": [{"object": "game_theory", "category": "corporate_finance", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "game_theory", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "educational_summary", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of game theory.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "medium_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.86, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "corporate_finance", "topic": "game_theory"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Game Theory", "description": "Game Theory explained briefly: Game Theory is a foundational concept in finance (corporate finance). Core principles include: market clearing via price adjustments, stochastic shocks and mean reversion, agent incentives and moral hazard. It is often expressed by relations such as IRR: NPV(IRR) = 0 and applied in contexts like valuation of firms and projects. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["market clearing via price adjustments", "stochastic shocks and mean reversion", "agent incentives and moral hazard", "marginal analysis", "opportunity cost reasoning"], "mathematical_formulas": ["IRR: NPV(IRR) = 0"], "key_applications": ["valuation of firms and projects", "market regulation and compliance"], "teaching_examples": ["Example: demonstrate arbitrage between two markets with price gap 0.02."], "difficulty_level": "advanced", "estimated_read_time_min": 7, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:02.120945Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000057", "data_type": "text", "content": {"input": "Explain regulatory arbitrage in time_series (educational).", "output": "Regulatory Arbitrage is a foundational concept in econometrics (time series). Core principles include: behavioral bias corrections, inventory flow balancing, opportunity cost reasoning. It is often expressed by relations such as NPV = Σ (Ct / (1 + r)^t) - C0 and applied in contexts like policy design and macro forecasting."}, "annotations": {"semantic_labels": [{"object": "regulatory_arbitrage", "category": "time_series", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "regulatory_arbitrage", "relation": "relates_to_domain", "entity_2": "econometrics"}], "intent_sentiment": {"intent": "model_specification", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of regulatory arbitrage.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "volatile"}, "quality_confidence": {"confidence_score": 0.97, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "econometrics", "subdomain": "time_series", "topic": "regulatory_arbitrage"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Regulatory Arbitrage", "description": "Regulatory Arbitrage explained briefly: Regulatory Arbitrage is a foundational concept in econometrics (time series). Core principles include: behavioral bias corrections, inventory flow balancing, opportunity cost reasoning. It is often expressed by relations such as NPV = Σ (Ct / (1 + r)^t) - C0 and applied in contexts like policy design and macro forecasting. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["behavioral bias corrections", "inventory flow balancing", "opportunity cost reasoning", "aggregate demand-supply interactions", "agent incentives and moral hazard"], "mathematical_formulas": ["NPV = Σ (Ct / (1 + r)^t) - C0"], "key_applications": ["policy design and macro forecasting", "valuation of firms and projects"], "teaching_examples": ["Example: compute NPV of a three-year project with cashflows [100,200,300]."], "difficulty_level": "intermediate", "estimated_read_time_min": 14, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:02.155913Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000058", "data_type": "text", "content": {"input": "Explain fiscal multiplier in financial_accounting (educational).", "output": "Fiscal Multiplier is a foundational concept in accounting (financial accounting). Core principles include: market clearing via price adjustments, agent incentives and moral hazard, network externalities and scale effects. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like asset allocation and portfolio rebalancing."}, "annotations": {"semantic_labels": [{"object": "fiscal_multiplier", "category": "financial_accounting", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "fiscal_multiplier", "relation": "relates_to_domain", "entity_2": "accounting"}], "intent_sentiment": {"intent": "educational_summary", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of fiscal multiplier.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "volatile"}, "quality_confidence": {"confidence_score": 0.95, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "accounting", "subdomain": "financial_accounting", "topic": "fiscal_multiplier"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Fiscal Multiplier", "description": "Fiscal Multiplier explained briefly: Fiscal Multiplier is a foundational concept in accounting (financial accounting). Core principles include: market clearing via price adjustments, agent incentives and moral hazard, network externalities and scale effects. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like asset allocation and portfolio rebalancing. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["market clearing via price adjustments", "agent incentives and moral hazard", "network externalities and scale effects", "inventory flow balancing", "optimization under constraints"], "mathematical_formulas": ["DebtRatio = Debt / (Debt + Equity)"], "key_applications": ["asset allocation and portfolio rebalancing", "credit scoring and loan provisioning"], "teaching_examples": ["Example: stress-test a bank's portfolio under 30% market drop."], "difficulty_level": "introductory", "estimated_read_time_min": 18, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:02.202301Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000059", "data_type": "text", "content": {"input": "Explain banking frictions in operations (educational).", "output": "Banking Frictions is a foundational concept in business (operations). Core principles include: opportunity cost reasoning, stochastic shocks and mean reversion, trade-offs between risk and return. It is often expressed by relations such as CAPM: E[R] = Rf + β (E[Rm]-Rf) and applied in contexts like risk management and hedging strategies."}, "annotations": {"semantic_labels": [{"object": "banking_frictions", "category": "operations", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "banking_frictions", "relation": "relates_to_domain", "entity_2": "business"}], "intent_sentiment": {"intent": "technical_explanation", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of banking frictions.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "medium_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.95, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "business", "subdomain": "operations", "topic": "banking_frictions"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Banking Frictions", "description": "Banking Frictions explained briefly: Banking Frictions is a foundational concept in business (operations). Core principles include: opportunity cost reasoning, stochastic shocks and mean reversion, trade-offs between risk and return. It is often expressed by relations such as CAPM: E[R] = Rf + β (E[Rm]-Rf) and applied in contexts like risk management and hedging strategies. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["opportunity cost reasoning", "stochastic shocks and mean reversion", "trade-offs between risk and return", "optimization under constraints", "network externalities and scale effects"], "mathematical_formulas": ["CAPM: E[R] = Rf + β (E[Rm]-Rf)"], "key_applications": ["risk management and hedging strategies", "operational process optimization"], "teaching_examples": ["Example: compare two portfolios using Sharpe ratio with returns 0.12 and 0.08."], "difficulty_level": "introductory", "estimated_read_time_min": 12, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:02.239849Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000060", "data_type": "text", "content": {"input": "Explain game theory in operations (educational).", "output": "Game Theory is a foundational concept in business (operations). Core principles include: market clearing via price adjustments, inventory flow balancing, aggregate demand-supply interactions. It is often expressed by relations such as Leontief Input-Output: x = (I - A)^{-1} f and applied in contexts like policy design and macro forecasting."}, "annotations": {"semantic_labels": [{"object": "game_theory", "category": "operations", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "game_theory", "relation": "relates_to_domain", "entity_2": "business"}], "intent_sentiment": {"intent": "model_specification", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of game theory.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "stable"}, "quality_confidence": {"confidence_score": 0.87, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "business", "subdomain": "operations", "topic": "game_theory"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Game Theory", "description": "Game Theory explained briefly: Game Theory is a foundational concept in business (operations). Core principles include: market clearing via price adjustments, inventory flow balancing, aggregate demand-supply interactions. It is often expressed by relations such as Leontief Input-Output: x = (I - A)^{-1} f and applied in contexts like policy design and macro forecasting. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["market clearing via price adjustments", "inventory flow balancing", "aggregate demand-supply interactions", "agent incentives and moral hazard", "information asymmetry effects"], "mathematical_formulas": ["Leontief Input-Output: x = (I - A)^{-1} f"], "key_applications": ["policy design and macro forecasting", "asset allocation and portfolio rebalancing"], "teaching_examples": ["Example: demonstrate arbitrage between two markets with price gap 0.02."], "difficulty_level": "introductory", "estimated_read_time_min": 17, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:02.283929Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000061", "data_type": "text", "content": {"input": "Explain transaction cost economics in strategy (educational).", "output": "Transaction Cost Economics is a foundational concept in business (strategy). Core principles include: aggregate demand-supply interactions, network externalities and scale effects, agent incentives and moral hazard. It is often expressed by relations such as IRR: NPV(IRR) = 0 and applied in contexts like credit scoring and loan provisioning."}, "annotations": {"semantic_labels": [{"object": "transaction_cost_economics", "category": "strategy", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "transaction_cost_economics", "relation": "relates_to_domain", "entity_2": "business"}], "intent_sentiment": {"intent": "technical_explanation", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of transaction cost economics.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "volatile"}, "quality_confidence": {"confidence_score": 0.89, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "business", "subdomain": "strategy", "topic": "transaction_cost_economics"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Transaction Cost Economics", "description": "Transaction Cost Economics explained briefly: Transaction Cost Economics is a foundational concept in business (strategy). Core principles include: aggregate demand-supply interactions, network externalities and scale effects, agent incentives and moral hazard. It is often expressed by relations such as IRR: NPV(IRR) = 0 and applied in contexts like credit scoring and loan provisioning. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["aggregate demand-supply interactions", "network externalities and scale effects", "agent incentives and moral hazard", "marginal analysis", "stochastic shocks and mean reversion"], "mathematical_formulas": ["IRR: NPV(IRR) = 0"], "key_applications": ["credit scoring and loan provisioning", "asset allocation and portfolio rebalancing"], "teaching_examples": ["Example: apply supply-demand shift to simulate tax incidence."], "difficulty_level": "advanced", "estimated_read_time_min": 17, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:02.325298Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000062", "data_type": "text", "content": {"input": "Explain platform economics in investments (educational).", "output": "Platform Economics is a foundational concept in finance (investments). Core principles include: optimization under constraints, inventory flow balancing, behavioral bias corrections. It is often expressed by relations such as Black-Scholes: C = S N(d1) - K e^{-rT} N(d2) and applied in contexts like pricing of derivatives and structured products."}, "annotations": {"semantic_labels": [{"object": "platform_economics", "category": "investments", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "platform_economics", "relation": "relates_to_domain", "entity_2": "finance"}], "intent_sentiment": {"intent": "technical_explanation", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of platform economics.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "short_term", "stability": "volatile"}, "quality_confidence": {"confidence_score": 0.89, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "finance", "subdomain": "investments", "topic": "platform_economics"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Platform Economics", "description": "Platform Economics explained briefly: Platform Economics is a foundational concept in finance (investments). Core principles include: optimization under constraints, inventory flow balancing, behavioral bias corrections. It is often expressed by relations such as Black-Scholes: C = S N(d1) - K e^{-rT} N(d2) and applied in contexts like pricing of derivatives and structured products. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["optimization under constraints", "inventory flow balancing", "behavioral bias corrections", "information asymmetry effects", "aggregate demand-supply interactions"], "mathematical_formulas": ["Black-Scholes: C = S N(d1) - K e^{-rT} N(d2)"], "key_applications": ["pricing of derivatives and structured products", "behavioral interventions for consumer finance"], "teaching_examples": ["Example: compute NPV of a three-year project with cashflows [100,200,300]."], "difficulty_level": "introductory", "estimated_read_time_min": 9, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:02.370744Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000063", "data_type": "text", "content": {"input": "Explain currency carry trade in microeconomics (educational).", "output": "Currency Carry Trade is a foundational concept in economics (microeconomics). Core principles include: network externalities and scale effects, opportunity cost reasoning, agent incentives and moral hazard. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like behavioral interventions for consumer finance."}, "annotations": {"semantic_labels": [{"object": "currency_carry_trade", "category": "microeconomics", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "currency_carry_trade", "relation": "relates_to_domain", "entity_2": "economics"}], "intent_sentiment": {"intent": "model_specification", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of currency carry trade.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "long_term", "stability": "volatile"}, "quality_confidence": {"confidence_score": 0.86, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "economics", "subdomain": "microeconomics", "topic": "currency_carry_trade"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Currency Carry Trade", "description": "Currency Carry Trade explained briefly: Currency Carry Trade is a foundational concept in economics (microeconomics). Core principles include: network externalities and scale effects, opportunity cost reasoning, agent incentives and moral hazard. It is often expressed by relations such as DebtRatio = Debt / (Debt + Equity) and applied in contexts like behavioral interventions for consumer finance. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["network externalities and scale effects", "opportunity cost reasoning", "agent incentives and moral hazard", "market clearing via price adjustments", "optimization under constraints"], "mathematical_formulas": ["DebtRatio = Debt / (Debt + Equity)"], "key_applications": ["behavioral interventions for consumer finance", "operational process optimization"], "teaching_examples": ["Example: compute NPV of a three-year project with cashflows [100,200,300]."], "difficulty_level": "introductory", "estimated_read_time_min": 15, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:02.419136Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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{"id": "finance_0000064", "data_type": "text", "content": {"input": "Explain capital structure in financial_accounting (educational).", "output": "Capital Structure is a foundational concept in accounting (financial accounting). Core principles include: behavioral bias corrections, information asymmetry effects, network externalities and scale effects. It is often expressed by relations such as Sharpe = (Rp - Rf) / σp and applied in contexts like policy design and macro forecasting."}, "annotations": {"semantic_labels": [{"object": "capital_structure", "category": "financial_accounting", "property": "theoretical_concept"}], "entity_relations": [{"entity_1": "capital_structure", "relation": "relates_to_domain", "entity_2": "accounting"}], "intent_sentiment": {"intent": "educational_summary", "sentiment": "neutral", "emotion": "analytical"}, "reasoning_steps": ["Define the core variables of capital structure.", "Explain the logical derivation or intuition step-by-step.", "Describe how to apply the concept to a simple example."], "error_annotations": {"hallucination": false, "bias": false, "factual_error": false, "grammar_issues": false}, "temporal_tags": {"applicability_horizon": "long_term", "stability": "context_dependent"}, "quality_confidence": {"confidence_score": 0.98, "verified_by": "algorithmic_review", "review_date": "2025-11-02"}, "domain_tags": {"domain": "accounting", "subdomain": "financial_accounting", "topic": "capital_structure"}, "instruction_quality": {"helpfulness": 9, "safety": 10, "factual_accuracy": 9, "coherence": 10}}, "metadata": {"theory_name": "Capital Structure", "description": "Capital Structure explained briefly: Capital Structure is a foundational concept in accounting (financial accounting). Core principles include: behavioral bias corrections, information asymmetry effects, network externalities and scale effects. It is often expressed by relations such as Sharpe = (Rp - Rf) / σp and applied in contexts like policy design and macro forecasting. This wording is algorithmically generated for educational purposes and avoids copying any single source.", "core_principles": ["behavioral bias corrections", "information asymmetry effects", "network externalities and scale effects", "inventory flow balancing", "stochastic shocks and mean reversion"], "mathematical_formulas": ["Sharpe = (Rp - Rf) / σp"], "key_applications": ["policy design and macro forecasting", "risk management and hedging strategies"], "teaching_examples": ["Example: stress-test a bank's portfolio under 30% market drop."], "difficulty_level": "introductory", "estimated_read_time_min": 20, "source": "Anode AI synthesized educational content (algorithmic)", "license": "CC-BY-4.0", "language": "en", "created_at": "2025-11-02T12:08:02.461288Z", "pipeline_version": "finance-gen-v1", "provenance": "synthetic_educational_generation"}}
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