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Record W4402192285 · doi:10.1139/facets-2023-0204

Fishing in turbulent waters: resilience, risk, and trust in British Columbia’s declining commercial salmon fishery

2024· article· en· W4402192285 on OpenAlexafffundvenueabout
E. Valle, Benjamin P. Neal, Ilse A. Martínez‐Candelas, Patrick Dann, Dawn Webb, Loren McClenachan

Bibliographic record

VenueFACETS · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsCanada Auto WorkersUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of CanadaCanada Research Chairs
KeywordsFisheryResilience (materials science)FishingFish <Actinopterygii>Environmental scienceBiology

Abstract

fetched live from OpenAlex

The impacts fishing communities face as a result of declining fisheries productivity and access may largely hinge on measurable attributes of their social resilience. Wild-origin Pacific salmon populations have been in a marked decline since the 1960s, resulting in progressively declining access for many commercial fisheries. More recent acute stressors have caused appreciable tribulation to commercial fishers in British Columbia, raising concern over their capacity to remain viable in the industry, and underscoring the need to examine the fishery under a social resilience framework. Here, we coupled an online survey instrument with in-depth interviews to assess commercial salmon fishers’ social resilience, socioeconomic characteristics, risk perceptions, trust in fishery management, and the relationships between these variables. Our results show that social resilience is low overall, with older, more experienced, and less diversified fishers being particularly vulnerable to declining salmon access. While 73% of fishers reported having plans to adapt to future declines in salmon access, 92% reported feeling that there are barriers impeding their adaptation, and 75% reported having no trust in fisheries management helping them adapt. Fishers’ social resilience was positively correlated with their trust in, and perceived trust from fisheries management.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.575
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 studies0.0000.000
Scholarly communication0.0020.001
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 head0.279
Teacher spread0.263 · 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

Citations3
Published2024
Admission routes4
Has abstractyes

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