Governance and Power Dynamics in a Small-Scale Hilsa Shad (Tenualosa ilisha) Fishery: A Case Study from Bangladesh
Bibliographic record
Abstract
This paper considers the hilsa shad (Tenualosa ilisha) fishery of southern Bangladesh as a case study regarding governance and power dynamics at play in a small-scale fishery, and the relevance of these for the sustainable management of coastal fisheries. Qualitative methods, involving in-depth individual interviews (n = 128) and focus group discussions (n = 8) with key stakeholders in the hilsa fishery, were used to capture multiple perspectives on governance from those in different positions in the relative power structures studied, while facilitating insightful discussions and reflections. The analysis here is based on a power cube framework along three power dimensions (levels, spaces, and forms) in Bangladesh’s hilsa fishery. The study displays an imbalance in the present hilsa governance structure, with some stakeholders exercising more power than others, sidelining small-scale fishers, and encouraging increasing illegal fishing levels that ultimately harm both the fisheries and those dependent on them. To overcome this, we propose a co-management system that can play a vital role in equalizing power asymmetry among hilsa fishery stakeholders and ensure effective hilsa fishery governance. Our results suggest that recognizing analyzed power dynamics has substantial implications for the planning and implementation of such co-management and the long-term sustainability of the hilsa fishery.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".