Canadian Fisheries Policy: Challenges and Choices Canadian Fisheries Policy: Challenges and Choices 133
Bibliographic record
Abstract
Les pêches océaniques posent de sérieux défis au Canada. Les problèmes comprennent l’effondrement du stock de poissons de fond de l’Atlantique au début des années 90, les conflits internationaux au sujet de la pêche en zones partagées et chevauchant les frontières, les conflits entre pêcheurs en compétition de même que les faibles revenus et la surcapitalisation dans plusieurs types de pêches. Nous présentons l’état actuel des pêches océaniques au Canada, évaluons sa gestion présente et passée pour prendre en considération ces problèmes et proposer des politiques visant à aider le Canada à profiter du plein potentiel de ses ressources maritimes. Canada faces grave challenges in its ocean fisheries. The problems include the collapse of Atlantic groundfish stocks in the early 1990s, international disputes over shared and straddling fisheries, conflicts among competing fishers, and low incomes and overcapitalization in many important fisheries. We assess the present state of Canada’s ocean fisheries, evaluate past and current management to address the problems and propose policies to help Canada realize the full potential from its marine resources. We have some of the world’s most valuable fish resources, they are capable of yielding great economic and social benefits; yet many commercial fishermen and fishing companies are near bankruptcy, sport fishermen and Indians are preoccupied with declining opportunities, and the fisheries are a heavy burden on Canadian taxpayers. Peter H. Pearse (1982, p. 3).
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.021 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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".