Impact of Enforcement and Co-Management on Compliance Behavior of Fishermen
Bibliographic record
Abstract
The purpose of this paper is to examine the factors believed to affect compliance behavior with regard to the zoning regulation of 284 Peninsular Malaysian fishermen. Frequent violation of regulations will have an impact on the demand for protection, and therefore lead to greater expenditure on law enforcement. The theoretical models of compliance behavior tested include the basic deterrent model, which focuses on the certainty and severity of penalty as a key determinant of compliance, and models which integrate economic theory with theories of social psychology to account for legitimacy, deterrence and other motivations expected to influence an individuals’ decisions on whether to comply. Policy makers who want to improve compliance face two choices: the first choice is whether to focus only on building staff capacity to detect and correct non-compliance; and the second choice is a combination of the strategies in building staff capacity and at the same time building commitment among fishermen so that they will comply with the regulations. The results of the empirical analysis provide evidence of the relationship between co-management strategies on the one hand, and types of fishermen on the other. These findings imply that co-management activities should be strengthened to complement the deterrent strategies in the management of fishery resources in Peninsular Malaysia.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".