Lost in translation: understanding divergent perspectives on a depleted fish stock
Bibliographic record
Abstract
Fishers commonly disagree with stock assessment results, particularly when a stock declines and strict harvest controls become necessary. Such regulations alter fisher perceptions of stock dynamics, contributing to a divergence in perspectives. Some assessments have inconsistent terminal year values (retrospective patterns) which fuel distrust in scientific advice. When assessment and fishery perspectives disagree, independent surveys can help identify biases and interpret discrepancies. We examine fishery trends and assessment results for Atlantic cod in the Gulf of Maine, a stock which has declined for decades. Trends were compared to a scientific industry cooperative trawl survey and a telephone survey of fisher perceptions. Trawl survey results generally corroborate the assessment perspective on population scale and decline, yet suggest a different view of the age structure. Fisher perceptions were at odds with the assessment and trawl survey and likely resulted from regulations that altered fisher behavior, causing catch rates to increase while the stock declined. Divergent perspectives may be an unavoidable consequence of fishery management, yet acknowledging the underlying mechanisms might help avoid future conflict.
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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 | 0.043 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".