Fishing in turbulent waters: resilience, risk, and trust in British Columbia’s declining commercial salmon fishery
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".