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Record W2034039962 · doi:10.1080/13504500009470053

Co-management in marine fisheries in Malalison Island, central Philippines

2000· article· en· W2034039962 on OpenAlexaboutno aff
Didi B. Baticados, Renato F. Agbayani

Bibliographic record

VenueInternational Journal of Sustainable Development & World Ecology · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFisheryFishingFisheries managementBusinessSustainabilityGeographyPopulationMarine protected areaMarine conservationCoral reefEquity (law)Environmental resource managementEnvironmental planningEconomicsEcologyPolitical science

Abstract

fetched live from OpenAlex

This study, conducted from November 1995 to February 1996, describes the evolution and impact of fisheries co-management arrangements in a coral reef fishing village at Malalison Island, central Philippines. The island is the site of a community-based fishery resources management project of the Southeast Asian Fisheries Development Center Aquaculture Department, funded by the International Development Research Centre of Canada.Using a case study approach and inferential statistics in the analysis of data, the CD. management arrangements on the island are perceived to be successful based on equity, efficiency and sustainability criteria. Fishers, represented by the Fishermen's Association of Malalison Island (FAMI) who form the core group, participated actively in the management of fishery resources with legal and financial support both from the municipal and barangay (village) government. Potential problems nonetheless, still exist with the ambivalent attitude of fishers toward rule-breaking, especially of fishery rules directly affecting them. The future of co-management arrangements will largely depend on how the fishers and other stakeholders maintain and build earlier initiatives with the eventual phasing out of SEAFDEC AQD from the island. The rapid population growth could also affect project gains.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.000

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 head0.005
GPT teacher head0.216
Teacher spread0.210 · 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.

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

Citations35
Published2000
Admission routes1
Has abstractyes

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