The Canadian <i>Species at Risk Act</i> at 20: an aquatic perspective
Bibliographic record
Abstract
I reflect upon how successful the implementation of the Canadian Species at Risk Act (SARA), enacted in 2002, has been at meeting its intended purposes of protecting and recovering at-risk species through the implementation of the five-step SARA process, particularly as it relates to aquatic species at risk. For each one of the steps, I identify shortcomings and provide recommendations to overcome those challenges. The overall implementation of the SARA process has fallen far short of incorporating Indigenous knowledge as outlined in Act, dealt poorly with climate change, and underestimated the need for Western science at each step in the process. Addressing these challenges would require large increases in funding, as current funding is woefully inadequate to undertake current legislated requirements of SARA, let alone the aspirational goals of actually recovering species. Despite these challenges, the Act we have is better than the alternative of no Act at all and that many species have benefitted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".