Social harvest control rules for sustainable fisheries
Bibliographic record
Abstract
Abstract Fisheries are supposed to be for the benefit of society, producing food, providing livelihoods and enabling cultural continuity. Biological productivity goals for fish stocks operationalised through Harvest Control Rules (HCRs) are central to contemporary fisheries management. While fisheries policies often state socio‐economic objectives, such as enhancing the livelihoods of coastal communities, those are rarely, if ever, incorporated into operationalised management procedures. The lack of articulation of social objectives and lack of monitoring of social outcomes around HCRs amounts to poor public policy. In this article, we explore the potential for social HCRs (sHCRs) with reference points and agreed predefined actions to make the social dimensions of fisheries explicit. sHCRs cannot cover all social dimensions, so should be considered as one tool within a broader framework of fisheries governance. Moreover, successful sHCRs would require sound deliberative and participatory processes to generate legitimate social objectives, and monitoring and evaluation of fisheries management performance against those objectives. We introduce two potential types of sHCRs, one based on allocation of catch within biological limit reference points, and one for when fishing exceeds biological limit reference points. The application of sHCRs, we argue, can foster accountability and help avoid non‐transparent negotiations on size and distribution of the catch. Our proposal is a call to action for policy makers and fisheries managers to properly integrate social criteria into fisheries governance, and for both biophysical fisheries scientists and social scientists to do better in practical collaboration for methods and knowledge development to support this integration.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".