Developing capacity of forest users through participatory forest management: Evidence from Madhupur Sal forest in Bangladesh
Bibliographic record
Abstract
Participatory forest management is credited for supporting social learning processes and fostering capacity of forest users for collaboration and collective actions. Despite more than a decade of practice, the empirical evidence substantiating the contribution of participatory management for the capacity development of forest users is scarce. This study assesses a participatory forest management program in Madhupur Sal forest, Bangladesh, by comparing the capacity of de-facto groups of participants and nonparticipants and identifies factors that influence the capacity development. Data were collected using a mixed method approach which combines both qualitative and quantitative methods of data collection. Results indicate that participants differed from nonparticipants significantly in terms of various capacity dimensions related to collective actions. Extension services, credit support, trust within society, information and communication influence the level of capacities in tribal population to adapt and respond to changes. The initiatives to manage natural resources are likely to be more successful if the forest management program initiators consider several factors that influence the capacity development of resource users.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".