Text mining policy: Classifying forest and landscape restoration policy agenda with neural information retrieval
Table 4. Accuracy, precision, recall, and F1-score for policy agenda classification across 31 policy documents in Malawi (n=12), Kenya (n=12), and Rwanda (n=7).
['Agenda', 'Accuracy', 'Precision', 'Recall', 'F1']
The methodology performed best on topics with a very narrow scope and little overlap with other agenda, such as the establishment of buffer zones, and worst on topics that are broad in nature, subjective, and have potential to overlap with other agenda, such as land use rights and forest protection. Overall, we report similar metrics across the three countries indicating that this approach is able to generalize to new contexts. Specifically, the F1-scores for Kenya, Malawi, and Rwanda were 0.85, 0.80, and 0.82, respectively.