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Record W2365617817 · doi:10.14288/1.0090099

The experience of women in the British Columbia fishery during a climate of crisis and change

2009· article· en· W2365617817 on OpenAlexaboutno aff
Patricia Anne Christie

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicFeminist Epistemology and Gender Studies
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeFisheryGeographyPolitical scienceOceanographyGeology

Abstract

fetched live from OpenAlex

The British Columbia fishery is in crisis. Environmental conditions and problems with the management of the fishing resource have led to a significant reduction in stocks and created serious economic problems in the industry. Women's work is central to the fishery yet it is often unpaid, underpaid and undervalued. Policies guiding the restructuring of the industry do not take into account the unique circumstances of women in the industry. Therefore, the purpose of this study is to improve the understanding about the ways fishery policy impacts the lives of women in the fishing communities of BC. The question posed: What is the experience of women in the BC Fisheries during a climate of crisis and change? A feminist approach is applied to this qualitative study. Unstructured interviews were conducted with a sample of nine women who have worked in the industry and are impacted by closures and cutbacks. Findings reveal a devastating magnitude of loss for these women and their families; a great mistrust of the motives of the Federal government and its policies; and a multitude of strategies used in their struggle for survival. The critical inequities in the fishing industry make this study particularly relevant to social work. Further research is warranted to develop adjustment, programs that address these inequalities and meet the needs of women in the coastal communities of BC. Limitations of thisstudy and suggestions for future research are discussed in the light of these findings.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.220
Teacher spread0.204 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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