Catch shares, the theory of cooperative games and the spirit of Elinor Ostrom: a research agenda
Bibliographic record
Abstract
This paper puts forth the proposition that all catch share schemes should be analysed primarily through the lens of cooperative game theory, which has now been developed to an advanced degree in the analysis of international fisheries management. If the fishers in a catch share scheme are playing cooperatively, the resource managers are at the same time to be seen as playing a leader-follower game with the fishers. While the proposition obviously applies to all catch share schemes, the focus of the paper will be on ITQ schemes. The basic rudiments of the required theory are to be found in a 2006 article by Lone Kronbak and Marko Lindroos, and carry with it the spirit of Elinor Ostrom. We will argue that much more needs to be done. We shall maintain that, if a given ITQ scheme constitutes a stable cooperative game, the various residual inefficiencies of ITQ schemes discussed in many articles should vanish. Needless to say, if a given ITQ scheme constitutes a stable cooperative game the distinction between it and other catch right schemes will blur. We shall also argue that, if ITQ schemes succeed as stable cooperative games, this will enable the fishers to bargain constructively with other stakeholders. Examples will be drawn, inter alia, from the evolving harvesting rights schemes off Canada’s Pacific coast.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".