Moving beyond binary metrics of compliance in small-scale fisheries
Bibliographic record
Abstract
Compliance mediates how policies affect fisheries and the people who depend upon them for work, food, and well-being. Many of the world’s small-scale fisheries lack clear, legal rules to prevent overfishing, or are subject to conflicting rules. Where clear rules do exist, noncompliance is widespread. Binary assessments of compliance, which frequently lead to calls for increased enforcement when compliance is low, tend to overlook the insights that can be gained from understanding how and why people comply or not. An emerging two-dimensional model that distinguishes between four ideal-types of compliance offers the opportunity to understand the motivations behind problems of illegality in natural resource management. We qualitatively operationalized this two-dimensional model of compliance using interview and survey data from four cases of small-scale fisheries regulation in Costa Rica, Mexico, Honduras, and Puerto Rico. The two-dimensional model explained social-ecological outcomes like fishery sustainability and marginalization of fishers, particularly where apparent compliance disguised corruption and disengagement. The two-dimensional model also accommodated incongruence between rules and the broader aims that they serve. Ultimately, the two-dimensional diagnostic was practical and useful for identifying diverse policy levers to address illegal fishing in addition to enforcement. This work advances our understanding of links among compliance, self-governance, justice, and sustainability of the commons.
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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 | 0.018 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".