Species Listing under Canada's Species at Risk Act
Bibliographic record
Abstract
In a preliminary analysis of listing decisions under Canada's Species at Risk Act (SARA), Mooers et al. (2007)demonstrated an apparent bias against marine and northern species. As a follow-up, we expanded the set of potential explanatory variables, including information on jurisdictional and administrative elements of the listing process, and considered an additional 16 species recommended for listing by SARA's scientific advisory committee as of 15 August 2006. Logistic model selection based on Akaike differences suggested that species were less likely to be listed if they were harvested or had commercial or subsistence harvesting as an explicitly identified threat; had Department of Fisheries and Oceans (DFO) as a responsible authority (RA); were located in Canada's north generally, and especially in Nunavut; or were found mostly or entirely within Canada. Subsequent model validation with an independent set of 50 species for which a listing decision was handed down in December 2007 showed an overall misclassification rate of <0.10, indicating reasonable predictive power. In light of these results, we recommend that RAs under SARA adopt a two-track listing approach to address problems of delays arising from extended consultations and the inconsistent use by the RAs of socioeconomic analysis; consider revising SARA so that socioeconomic analysis occurs during decisions about protecting species and their habitats rather than at the listing stage; and maintain an integrated database with information on species' biology, threats, and agency actions to enable future evaluation of SARA's impact.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".