Sustainable Management of Sundarbans: Stakeholder Attitudes Towards Participatory Management and Conservation of Mangrove Forests
Bibliographic record
Abstract
The Sundarbans is one of the oldest, contiguous, and systematically managed mangroves in the world. This biologically diverse ecosystem provides numerous benefits and services to local communities and the environment, however, it continues to remain under threat from population pressure, overexploitation, natural disasters and lack of practical policy regimes. This study assesses attitudes of local stakeholders towards sustainable management and conservation of mangrove forests as a means to assist planners, policy-makers, and decision-makers. A mixed-method approach was conducted to fulfill the objectives of this study. The study reveals that the people of the Sundarbans Impact Zone are highly dependent on the Sundarbans for their livelihood. Indeed, collecting resources from both aquatic and terrestrial areas is considered a traditional right. As such, people are increasingly becoming more conscious about government policy, and they want the forest to be managed sustainably. Generally, the language of governance is very strong, but many argue that implementation of policy is difficult because of competing policies, weak infrastructure, inefficiencies, illegal approaches, and corruption. Efforts should be made to develop and advance coupled human-environment (socio-ecological) systems that call for more participatory management approaches. Wider participation and ‘empowerment’ of stakeholders would improve the governance of the Sundarbans and ensure common priorities and levels of agreement on both conservation and livelihood issues.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".