Privatization in Fisheries: Lessons From Experiences in the U.S., Canada, and Norway
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it