Hallmarks of science missing from North American wildlife management
Bibliographic record
Abstract
Resource management agencies commonly defend controversial policy by claiming adherence to science-based approaches. For example, proponents and practitioners of the "North American Model of Wildlife Conservation," which guides hunting policy across much of the United States and Canada, assert that science plays a central role in shaping policy. However, what that means is rarely defined. We propose a framework that identifies four fundamental hallmarks of science relevant to natural resource management (measurable objectives, evidence, transparency, and independent review) and test for their presence in hunt management plans created by 62 U.S. state and Canadian provincial and territorial agencies across 667 management systems (species-jurisdictions). We found that most (60%) systems contained fewer than half of the indicator criteria assessed, with more criteria detected in systems that were peer-reviewed, that pertained to "big game," and in jurisdictions at increasing latitudes. These results raise doubt about the purported scientific basis of hunt management across the United States and Canada. Our framework provides guidance for adopting a science-based approach to safeguard not only wildlife but also agencies from potential social, legal, and political conflict.
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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 | 0.250 | 0.339 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.018 | 0.060 |
| Scholarly communication | 0.027 | 0.018 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".