To protect or forget? Comparing species at risk legislation across Canada’s common-law provinces
Bibliographic record
Abstract
Species across the globe are at risk of disappearing due to a human-driven extinction crisis. One of the most important tools for combating biodiversity loss is species at risk legislation. Of Canada’s nine common law provinces, only five have enacted legislation specifically designated to address species at risk protections. The other four have their protections included within nonspecies at risk laws and regulations. In this research, we compared the content and completeness of species at risk protections among the provinces. To achieve this, we assessed the language of each province’s legislation using statutory interpretation and legal analysis and made a rubric to assign numerical scores to each province based on the thoroughness of their protections. We also examined how many provinces that had listed species at risk had corresponding conservation plans, a demonstration that a government is taking action to protect and conserve at risk species. We find that jurisdictions with designated species at risk legislation significantly outscore those without designated legislation. This indicates that they have more comprehensive and stronger protections for species at risk.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".