Fisheries Co-Management and Legal Pluralism: How an Analytical Problem Becomes an Institutional One
Bibliographic record
Abstract
This paper addresses two issues pertaining to legal pluralism in capture fisheries, particularly with regard to the South. First there is the problem of analysis. If legal pluralism is a common phenomenon, how is it to be discerned and understood? Secondly, there is the matter of institutional design: given the pervasiveness of legal pluralism, which management institutions are better suited to represent and resolve inter-legal system differences? The authors argue the case of co-management. Drawing on examples and insights from a comparative research project in South Asia, four basic types of legal pluralism and co-management are distinguished. The authors conclude that co-management is a process that brings legal systems, and their constituent organizations and groups, together within a single framework. For fisher organizations, which frequently have distinct legal perspectives, co-management is an essential path to legitimacy. For the state, other legal systems are a resource that management can draw upon.
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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 | 0.051 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.018 | 0.139 |
| Scholarly communication | 0.027 | 0.052 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".