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Record W2161080018 · doi:10.5539/jas.v5n6p164

Property Rights and Access: the Case of Community Based Fisheries Management in Bangladesh

2013· article· en· W2161080018 on OpenAlexvenueno aff
Gazi Md. Nurul Islam, Tai Shzee Yew

Bibliographic record

VenueJournal of Agricultural Science · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLand Rights and Reforms
Canadian institutionsnot available
FundersDepartment for International Development
KeywordsLivelihoodBusinessSustainabilityFishingFisheryFisheries managementFloodplainGeographyAgricultureEcology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.939
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.230
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2013
Admission routes1
Has abstractyes

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