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Record W4290725355 · doi:10.33997/j.afs.2008.21.3.001

Understanding Culture-based Fisheries: An Assessment of a Community-Managed Beel Fisheries in Bangladesh

2008· article· en· W4290725355 on OpenAlexafffund
A.A. MAMUN, C.E. HAQUE

Bibliographic record

VenueAsian Fisheries Science · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFish Biology and Ecology Studies
Canadian institutionsUniversity of Manitoba
FundersInternational Development Research Centre
KeywordsFisheries managementFisheries lawBusinessResource (disambiguation)FisheryCitizen journalismResource management (computing)Environmental resource managementEnvironmental planningEconomicsPolitical scienceFishingGeography

Abstract

fetched live from OpenAlex

This paper presents a critical review of culture-based fisheries under the framework of a co-management arrangement of this resource in Bangladesh. It is recognized that the research and practice of culture-based fisheries are largely focused on production and put emphasis mainly on the economic aspects that relate to investment returns and cost-effectiveness of culture fisheries. In the resource management regimes of recent decades, little attention has been given to other crucial management, societal and environmental aspects. In consideration of these perspectives, the study attempts an empirical analysis of community-based fisheries management (CBFM), with a focus on the assessment of its role in facilitating the development of culture-based fisheries. By adopting a participatory approach to resource management, this research examines several critical aspects of management related to issues of property rights, the leasing out of public land and water bodies, and institutional linkages. It also pays due attention to related social considerations under a community-managed, culture-based fisheries program in Bangladesh. It is argued that culture-based fisheries will not achieve their goals without the adoption of a wide-encompassing management strategy.

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 categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.999

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 studies0.0020.005
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 head0.121
GPT teacher head0.283
Teacher spread0.161 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2008
Admission routes2
Has abstractyes

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