Fishing Rights as an Example of the Economic Rhetoric of Privatization: Calling for an Implicated Economics*
Bibliographic record
Abstract
Au cours des dernières années, toutes les sciences sociales ont eu à produire des travaux de recherche aux répercussions d'ordre public. Mais dans quelle mesure ces sciences sociales devraient‐elles inter‐venir dans le domaine de l'ordre public quand leurs recommandations dans ce domaine créent des situations inattendues et préjudiciables? Dans cet article, nous nous penchons sur cette question en étudiant l'exemple de l'économie et des modèles de droits privés de propriété dans les pêcheries des provinces de l'Atlantique. Ces modèles sont comparés et mis en contraste avec les modèles anthropologiques et juridiques afin de montrer dans quel domaine et pour quelle raison l'économie s'est égarée dans l'élaboration de modèles de droits de propriété sur les ressources halieutiques. De ce fait, les recommandations de politique économique en matière de droits de propriéte dans l'industrie de la pêche sont erronées. En conclusion, nous proposons que les économistes soignent leur rhétorique afin de susciter des attentes et de créer des solutions qui donnent un caractère plus raisonnable à leurs recommandations. In recent years, all the social sciences have come under pressure to produce research that has public policy implications. But how implicated should those social sciences be when their policy advice leads to unexpected and perhaps detrimental outcomes? This paper explores this issue using the example of economics and private property rights in the Canadian Maritime fisheries. It compares and contrasts economic models of property rights with those in anthropology and law to show where and why economics has gone astray in its fish property rights models. It suggests that, having gone astray, economic policy advice on fisheries property systems is flawed. It concludes that economists should pay more attention to the role of their rhetoric in the construction of expectations and outcomes that make their recommendations seem the more reasonable.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| 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".