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Record W2772009877 · doi:10.34309/jp.v22i4.205

Women in Gendered Fisheries: Roles, Issues and Challenges in Cambodia, Indonesia, Vietnam and Philippines

2017· article· en· W2772009877 on OpenAlexaboutno aff
Ma. Linnea Villarosa-Tanchuling

Bibliographic record

VenueJurnal Perempuan · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
Fundersnot available
KeywordsEmpowermentFisheryGovernment (linguistics)Economic growthGeographyNatural resourceShoreEquity (law)Quarter (Canadian coin)Southeast asiaMarine conservationPolitical scienceBusinessSocioeconomicsSociologyEthnologyEconomics

Abstract

fetched live from OpenAlex

This paper is a synthesis of the results of the case studies on women’s situation in fisheries done by the members of the SEA Fish for Justice Network. The network is composed of 15 non-government and fishers organizations from the Southeast Asia region. It envisions equity in access to and control over off-shore, coastal and inland aquatic natural resources including the termination of suffering caused by unsustainable resources and/or privatized control over communal resources. The case studies were conducted by SEAFish Network members in Cambodia, Indonesia, Vietnam and Philippines in the second and third quarter of 2008 to highlight the roles, issues and challenges faced by women in coastal communities as well as the spaces provided them to facilitate their empowerment. The network members who conducted the studies were FACT (Cambodia), KIARA (Indonesia), MCD (Vietnam) and PROCESS-Bohol, CERD, and Tambuyog Development Center (CERD).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.005
Scholarly communication0.0040.004
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.039
GPT teacher head0.240
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2017
Admission routes1
Has abstractyes

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