Taxonomic biases persist from listing to management for Canadian species at risk
Bibliographic record
Abstract
Management planning for Canadian species at risk of extinction begins with recommendation for legal protection under the Species at Risk Act (SARA), and ends with Action Plans that guide management implementation. Roughly five years after the enactment of SARA in 2002, multiple studies identified taxonomic biases associated with the SARA listing process. Here, we provide a comprehensive test of whether taxonomic biases remain over a decade later. We also test whether biases in listing are propagated through to management implementation. We find that birds, reptiles and plants are more likely to be legally protected than other species. Arthropods and fishes are less likely to be protected, with unlisted fish species being twice as likely to be threatened by resource use than other unlisted species. We also find that arthropods and amphibians are less likely to have Action Plans than other species. In addition, we find no evidence that biases in listing or management have improved over time. Canadian species at risk recovery programs appear to be biased both in legal protection and management, disfavouring arthropods, amphibians and harvested fishes. If SARA is to fulfil its stated purpose, such biases must be directly addressed, through a transparent and formalised prioritisation system.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".