Assessing species at risk legislation across Canadian provinces and territories
Bibliographic record
Abstract
Canada’s provinces and territories govern species at risk across most of Canada, with the federal Species at Risk Act generally covering only aquatic species, migratory birds, and species living on federal land. More than a decade after a 2012 report by the environmental law charity Ecojustice on species at risk protection in Canada, we use the same criteria to evaluate the current state of provincial and territorial species at risk legislation, and we provide updates on changes in each jurisdiction since 2012. These criteria are as follows: whether at-risk species are being identified, whether these species are being protected, whether their habitat is being protected, and whether species recovery plans are being created and implemented. We find that there is considerable variation across jurisdictions, with shortcomings that result in inadequate protections for at-risk species, as well as strong components that should be adopted by all jurisdictions. We recommend seven key areas for improvement: dedicated and harmonized legislation, limited discretionary power, increased embrace of scientific and Indigenous knowledge, appropriate timelines for actions, reasonable exemptions to protections, habitat protection across land ownership types, and transparency throughout the process. We urge policymakers to address current shortcomings as they work toward meeting Canada’s biodiversity conservation commitments.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".