Changing paradigms in a changing climate: adaptive innovation towards forest management institutions to manage tropical forest in South and Southeast Asia
Bibliographic record
Abstract
Some communities in the tropics traditionally protect natural habitats for their cultural and material sources, for example, in a form of sacred sites and as a communal forest. These natural habitats play an important role in biodiversity conservation which maintain through indigenous institutions that do not require involvement of conservation organizations or government bodies. These indigenous institutions regulate through customary laws and belief systems led by community elders and traditional religious leaders. Evidence from our three research sites in south and south east Asia i.e., Matiranga in eastern Bangladesh, central Maluku in eastern Indonesia, and Palawan in the Philippines are presented here to highlight on these accounts. We used different methods i.e., participatory rural appraisal, personal observations, focus groups discussion and content analysis to elicit knowledge of the communities on how they conserve and manage their forests. Our result indicates that existing indigenous forest management institutions which are closely related to local people’s belief systems served as enabling agents to manage forests. These belief systems play an important role in monitoring of forest uses, and community leaders use to impose sanctions on the transgressors. As a conclusion, reinforcing these indigenous institutions is one of the forest management alternatives to mitigate future deforestation and degradation in the tropics. Indigenous management institutions can be strengthened by 1) backing their culture and conservation activities through giving recognition at government level, 2) establishing sustainable livelihood sources for communities living around the forests that can mitigate the overexploitation. Paper summary on page 439.||
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".