Property Rights and Access: the Case of Community Based Fisheries Management in Bangladesh
Bibliographic record
Abstract
The revenue oriented approach has not been able to involve poor fishers in the inland fisheries management in Bangladesh. A community-based fisheries management (CBFM-2) approach was implemented over a period of 6 years (2001-2006) to improve access to fishing rights of the poor and to improve productivity as well as sustainability of fisheries resources. This study investigates the changes in fisher’s access to livelihoods in the various types of water bodies such as closed beels (deeper depressions in the floodplain), open beels (lake), rivers and floodplains to enhance their livelihoods. Data for the study was obtained from two questionnaire-based field surveys conducted by the Bangladesh CBFM project office: a baseline study carried out in 2002 and an impact study in mid-2006. A total of 2,826 households were randomly selected from several regions in Bangladesh, comprising 1,994 households at 34 (51%) CBFM project water bodies and 832 households at 10 (59%) control water bodies. This study found that the CBFM fishers have obtained greater access to fisheries and improved livelihoods than non-CBFM fishers. The fishers have now changed their attitudes, have greater awareness of fisheries rules and are able to resolve conflicts much easier in the CBFM water bodies. Long term access rights over fisheries resources should be considered as the priority for a sustainable inland fishery and livelihoods of fishers 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".