Assessment of a pluralistic advisory system: the case of Madhupur Sal Forest in Bangladesh
Bibliographic record
Abstract
Purpose: Participatory forest management initiatives in Madhupur Sal Forest in Bangladesh are characterized by the coexistence of diverse types of advisory service providers. Despite a decade of implementation of forest management initiatives, an assessment of the service delivery from a pluralistic advisory service framework is not evident. Drawing on the framework of pluralistic advisory service this study aimed to assess the role and performance of different advisory service providers that contribute to the management of forest resources.Methodology: We employed a stakeholder analysis to identify different advisory service providers, along with their power relations with forest dwellers. Data were collected using a participatory workshop and nine interviews.Findings and practical implications: The study found that most of the advisory service providers did not ensure the quality of the services, and did not orient their services towards the needs and demands of the forest dwellers. The advisory service providers continued to work with a coordination gap among themselves and the forest dwellers, which ultimately hindered their collective efforts to mobilize resources and build the strong relational condition necessary for the proper management of forest resources.Theoretical implications: This study applied a ‘best fit’ framework to the micro-level case of a forest advisory service, which helped to explore the dynamics of an advisory system linked to forest management initiatives.Originality: This is the first attempt to assess a pluralistic advisory system for forest management in Bangladesh.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".