Institutional Interplay in Natural Resources Governance: Toward a Sub-Sectoral Approach for Medicinal Plants Management in Bangladesh
Bibliographic record
Abstract
Recognizing the significance of medicinal plants for rural livelihoods and primary healthcare, this paper attempted to analyze institutional interplays in medicinal plants management in Bangladesh. It assessed the governing process of natural resources by identifying cross-scale linkages of the institutions involved with managing medicinal plants. The study intended to delineate the interactional patterns and dynamics between existing formal and informal organizations toward exploring prospects of new medicinal plants governance institutions. Employing case study and participatory approaches to empirical field investigation, two intervention cases of the Livelihood and Agro-Forestry (LEAF) and Sustainable Environmental Management Program (SEMP) were assessed in two different social-ecological settings of the country. Involving 45 respondents in each site, Focus Group Discussions were carried out, and a total of 26 Key Informants were interviewed. The findings have revealed that undefined roles and responsibilities, inadequate coordination, and weak linkages among the cross-scale institutions resulted in ineffective management and relatively poor performance. Institutions with direct or indirect involvement in the process of managing medicinal plants interacted haphazardly, without much focus on the subsector and its local producers. Addressing the weaknesses, this study calls for formulating a national sub-sectoral approach focusing on strengthening and sustaining local producers and value addition to producer levels. Finally, this research offers a framework for developing a multi-stakeholder forum to govern medicinal plant resources coherently and effectively in Bangladesh.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".