Protecting Endangered Species in the US and Canada: The Role of Negative Lesson Drawing
Bibliographic record
Abstract
Abstract.Although the US and Canada share ecosystems, with many species ranging freely across the border, the two countries have taken very different approaches to protecting endangered species. The USEndangered Species Act, adopted in 1973, relies primarily on regulation, thus imposing the costs of protecting biodiversity on the private sector. In contrast, Canada'sSpecies at Risk Act, adopted in 2002, relies primarily on public expenditures to support stewardship programs. We argue that this difference is best explained by negative lesson drawing from the US experience. In particular, awareness of the costs of species protection in the US led Canadian business to present stronger opposition to regulation than had their American counterparts decades earlier. We use the case of the CanadianSpecies at Risk Actto theorize about conditions under which negative lesson drawing is likely to be most influential. Résumé.Bien que les États-Unis et le Canada partagent les mêmes écosystèmes, les deux pays ont adopté des approches très différentes en matière de protection des espèces en péril. La Loi américaine sur les espèces en péril (US Endangered Species Act), adoptée en 1973, porte essentiellement sur la régulation, et de ce fait impose les coûts de la protection de la biodiversité au secteur privé. En revanche, la Loi canadienne sur les espèces en péril, adoptée en 2002, fait principalement retomber les coûts des programmes de gestion au secteur public. Nous démontrons que cette différence s'explique principalement par le rôle des leçons négatives apprises de l'expérience des États-Unis. La prise de conscience des coûts liés à la protection des espèces en péril aux États-Unis a notamment amené les milieux d'affaires canadiens à présenter une plus forte opposition à la régulation que leurs homologues américains l'avaient fait des années plus tôt. En s'appuyant sur le cas de la Loi canadienne sur les espèces en péril, nous visons à théoriser les conditions selon lesquelles l'acquisition de connaissance par leçons négatives (“ negative lesson drawing ”) est susceptible d'être le plus concluant.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".