Co‐management in <scp>L</scp>atin <scp>A</scp>merican small‐scale shellfisheries: assessment from long‐term case studies
Bibliographic record
Abstract
Abstract Co‐management (Co‐M), defined as the sharing of management tasks and responsibilities between governments and local users, is emerging as a powerful institutional arrangement to redress fisheries paradigm failures, yet long‐term assessments of its performance are lacking. A comparative analysis of five small‐scale Latin American shellfisheries was conducted to identify factors suggesting success and failure. In Chile, Uruguay and Mexico Co‐M produced positive effects, including stabilization of landings at low levels, increase in abundance, CPUE, unit prices and revenues per unit of effort, and reduced interannual variability in several fishery indicators, particularly in landings. Co‐M was successful because it was mainly bottom‐up implemented and accompanied by‐catch shares (spatial property rights and community quotas). By contrast, Co‐M implementation was unable to prevent the collapse of the Galapagos sea cucumber fishery, as reflected by a decrease in abundance and CPUE. Negative effects were also observed in the Galapagos spiny lobster fishery during Co‐M implementation. However, recovery was observed in recent years, reflected in a stabilization of fishing effort and the highest CPUE and economic revenues observed since the beginning of the Co‐M implementation phase. The combined effects of market forces, climate variability and a moratorium on fishing effort were critical in fishery recovery. We conclude that Co‐M is not a blueprint that can be applied to all shellfisheries to enhance their governability. These social–ecological systems need to be managed by jointly addressing problems related to the resources, their marine environment and the people targeting them, accounting for their socioeconomic and cultural contexts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".