Learning from positive deviants in fisheries
Bibliographic record
Abstract
Abstract Despite progress in the management of assessed fish populations, many countries lag behind international commitments to restore overexploited stocks to healthy abundances. Here we use a mixed‐methods positive deviance approach, also known as ‘bright spot’ analysis, to understand what drives the successful governance of exploited species by learning from positive outliers, or ‘deviants’. We use Canada as a case study, identifying factors driving the abundance of 230 commercially exploited fish and invertebrate populations, of which only 28% were classified at healthy abundance in 2022. We first applied a generalized linear model to test how diverse socio‐ecological fishery attributes relate to stock health. We found healthier stocks are positively and significantly correlated with certain management regions, more selective gears, eco‐certification, and high fishery value. Counterintuitively, healthier stocks were also associated with high inherent fishing vulnerability and the absence of reference points. We then used fishery expert surveys and interviews to investigate the social and institutional characteristics of stocks healthier than expected, given their circumstances. We found that fisheries targeting these positive outliers have lower conflict among users, balanced stakeholder involvement in data collection and decision‐making, and improved accounting of mortality sources. Lessons from these positive deviants can be applied to improve underperforming management systems that are struggling to reverse overexploitation in Canada and elsewhere. More generally, we suggest that a positive deviance approach, already used in public health, could be a promising tool to learn about successful fisheries management interventions, and the diverse actors responsible for ensuring these interventions are successful.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".