No forest, no dispute: the rights-based approach to creating an enabling environment for participatory forest management based on a case from Madhupur Sal Forest, Bangladesh
Bibliographic record
Abstract
This study explored whether and how the duty-bearer applied a rights-based approach (RBA) in the context of long-running disputes in Madhupur Sal Forest, Bangladesh to transform conflicts into solutions for collective management of forest resources. Using a case study design, we applied a timeline method and semi-structured in-depth interviews to collect data. The grounded theory approach was used to reconstruct the experiences of tribal forest dwellers, and identify the common themes of RBA. The study revealed that neglecting the rights of the forest dwellers led to ineffective policies and programs and, subsequently, to long-running conflicts. In order to sustain collaboration, it is necessary to integrate rights-based discussions with desired recognition, promises, instruction, and welfare provision, considering freedom, security, need for information, and delegating responsibilities. The study provides insights into how forest duty-bearers should consider the broader perspective of RBA in order to sustain their initiatives and achieve the conservation goal.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.019 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".