Fixing the Canadian <i>Species at Risk Act</i>: identifying major issues and recommendations for increasing accountability and efficiency
Bibliographic record
Abstract
Since the implementation of the Canadian Species at Risk Act (SARA) in 2003, deficiencies in SARA and its application have become clear. Legislative and policy inconsistencies among responsible federal agencies and the use of a subjective approach for prioritizing species protection lead to taxonomic biases in protection. Variations in legislation among provinces/territories and the reluctance of the federal government to take actions make SARA’s application often inefficient on nonfederally managed lands. Ambiguous key terms (e.g., critical habitat) and disregard for legislated deadlines in many steps impede the efficacy of SARA. Additionally, the failure to fully recognize Indigenous knowledge and to seek Indigenous cooperation in the species protection process leads to weaker government accountability, promotes inequity, and leads to missed opportunities for partnerships. New legislative amendments with well-defined and standardized steps, including an automatic listing process, a systematic prioritization program, and clearer demands (e.g., mandatory threshold to trigger safety net/emergency order) would improve the success of species at risk protection. Moreover, a more inclusive approach that brings Indigenous representatives and independent scientists together is necessary for improving SARA’s effectiveness. These changes have the potential to transform SARA into a more powerful act towards protecting Canada’s at-risk wildlife. (The graphical abstract follows.)
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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 | 0.050 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".