Strong collective action enables valuable and sustainable fisheries for cooperatives
Bibliographic record
Abstract
Abstract Seafood is one of the most internationally-traded food commodities. International markets can provide higher revenues that benefit small-scale fishing communities but can also drive a decline in fished populations. Collective action in collective organizations such as fishing cooperatives is thought to enhance the sustainability of fished populations. However, our knowledge of how collective action enables fishing cooperatives to achieve positive social-ecological outcomes is dispersed across case studies. Here, we present a quantitative, national-level analysis exploring the relationship between different levels of collective action and social-ecological outcomes. We found that strong collective action in Mexican lobster cooperatives was related to both sustaining their fisheries and benefiting from international trade. In the 15 year study period, lobster cooperatives that demonstrate characteristics associated with strong collective action captured benefits from trade through high catch volumes and revenue. Despite lower (but stable) average prices, the biomass of their lobster populations was not compromised to reap these benefits. Individual case studies previously found that fishing cooperatives can support both positive social and ecological outcomes in small-scale fisheries. Our results confirm these findings at a national level and highlight the importance of strong collective action. Thus, our work contributes to a better understanding of the governance arrangements to promote fishing communities’ welfare and benefits from international trade and, therefore, will be invaluable to advancing small-scale fisheries governance.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".