A Tale of Two Acts: Endangered Species Listing Practices in Canada and the United States
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
Canada's Species at Risk Act (SARA) and the US Endangered Species Act (ESA) have adopted different approaches to achieve overlapping goals. We compare the ESA and SARA, focusing on the roles of science and policy in determining which species warrant legal protection. Our analysis suggests that each act could benefit from mimicking the strengths of the other, and both could be strengthened by greater clarity and transparency of listing determinations. A particular strength of SARA is that all evaluations of species' status are conducted by a single national scientific body. The ESA does not involve a comparable national body but has more stringent legal deadlines for listing actions, and listing decisions cannot by law consider socioeconomic factors (as can occur under SARA). The conservation of biodiversity would be enhanced if both acts were complemented by additional programs focused on broader efforts that protect more species before individual intervention is needed.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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