Climate and Cooperation: A New Perspective on the Management of Shared Fish Stocks
Bibliographic record
Abstract
Climate regime shifts occur at irregular intervals and have profound and persistent impacts on ocean temperature and circulation patterns and on the dynamics of marine fish populations. Despite a growing scientific literature and some attention to the implications of such regime shifts for domestic fisheries, the issue has received little attention in the context of international fishery management. This paper presents evidence for the significance of climatic regime shifts, and draws upon the recent history of conflict between Canada and the United States over Pacific salmon management to illustrate the dangers that unpredicted, unanticipated environmental regime shifts pose for efforts to maintain international cooperation. This suggests a need for greater attention to this issue. Fishery agreements can be made more resilient to the impacts of such environmental changes by explicitly building in flexibility — for example, by allowing the use of side payments. In addition, pre-agreements on procedures to be followed in the event of sustained changes in fish stock productivity or migration patterns, and cooperation on developing common scientific understandings, can help to prevent destructive conflicts. Finally, the literature employing game theoretic shared-fishery models could be further developed to focus on providing practical guidance for maintaining cooperation in the presence of unpredictable and persistent environmental changes.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".