Enforcement, deterrence, and compliance in co-managed small-scale fisheries
Bibliographic record
Abstract
Small-scale fisheries contribute nearly half the world’s seafood supply, yet the majority suffer from a lack of effective management, resulting in a threat to food security and ecosystem health. Co-management has been proposed as a solution to many of the problems these fisheries face (i.e., reduced catches, threats to food security, climate change, lack of effective monitoring and management, etc.), resulting in a large body of literature dedicated to its role in small-scale fisheries and the attributes linked to success. However, there is still little understanding of the role of enforcement, deterrence, and the attributes needed for compliance to occur in local settings. Using a modified framework borrowed from law and criminology science, we performed a systematic literature review to explore mechanisms associated with detection, detention, and deterrence in small-scale, co-managed fisheries. This review sheds light on the diverse approaches being used for enforcement and deterrence worldwide, the context surrounding these fisheries, compliance with management rules, and the attributes of co-management that align with successful compliance, enforcement, and deterrence. We found that 83% of the reviewed case studies included at least one mechanism used for detection, detention, and deterrence, and most cases with high compliance featured multiple types of mechanisms. Furthermore, the use of informal community-based mechanisms was more extensive than the use of formal mechanisms. Our study suggests that a combination of formal and informal enforcement and deterrence mechanisms enhances compliance with regulations. Although attributes such as the presence of leaders, strong social capital, and the presence of protected areas are important components of co-managed fisheries, further empirical research is needed to determine whether these attributes lead to enhanced compliance when different enforcement (i.e. detection and detainment) and deterrence mechanisms are present. This review is the first step toward understanding enforcement and deterrence, and their relationship with compliance from a holistic perspective. It is an attempt to motivate the scientific community to comprehensively document compliance, enforcement, and deterrence mechanisms in co-managed fisheries moving forward.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".