Privatization in Fisheries: Lessons From Experiences in the U.S., Canada, and Norway
Bibliographic record
Abstract
"In our research on fisheries management in Canada, Norway, and the United States, and in particular into systems of governance and market-oriented systems of resource allocation, we deal with the politics of conservation in several ways. The first concerns the politics of deciding for or against major institutional change. In all three countries, attempts to create so-called ITQs, or individual transferable quotas, in major commercial fisheries have been fraught with delay and controversy, largely because of the distributional issues raised by privatization and recourse to market-based regulation. The second concerns the structure of decision making, and in particular how user groups and their interests and concerns are and are not brought into the decision-making process. The third concerns the distributional effects of changes in fisheries property rights and how people respond to them. In this paper we touch upon aspects of these topics with particular emphasis on the hypothetical intersection of privatization and co-management."
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.024 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 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".