Multilevel governance and fisheries commons: Investigating performance and local capacities in rural Bangladesh
Bibliographic record
Abstract
This study presents a post-facto evaluation of the local capacity development processes used under co-management of fisheries and other resources of southern Bangladesh. It answers the question of how supportive were the capacity development tools used in implementing co-management. An 18 month study was conducted and six cases were investigated to understand the approaches to co-management programs used to develop local capacity. Founded in pragmatism and viewing co-management through a governance lens, a comparative case study method was used that combined both qualitative and quantitative research approaches for data collection and subsequent analysis. This study provides empirical evidence that co-management programs have applied a number of strategies (e.g. human resource and economic development) to enhance local capacities. However, these strategies have achieved mixed results with regard to developing governance that supports livelihoods. Training provided to develop human resources and economic capacity were not useful for fishers or had little lasting effects on fisheries development due to poor monitoring and a disconnection with the needs of local users. This study concludes that comanagement can facilitate local capacity but in order to realize the full potential of this approach we must address the issues of inappropriate technologies for training, the financial barriers to fishers with low cash income, and uneven power relationships among stakeholders, to create an enabling environment for effective modern governance of the fisheries commons. Our findings indicate a needsbased approach to capacity building is needed in order to support the livelihoods of local users through co-management
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".