Canada's Marine Species at Risk: Science and Law at the Helm, but a Sea of Uncertainties
Bibliographic record
Abstract
This article examines, through a three part format, Canada's legislative “lifeboat” for saving species from extinction, the Species at Risk Act (SARA), and how it has fared in its first two years of implementation with a focus on efforts to protect marine fish species. Part I explores how SARA has notionally placed science and law at the helm in the quest to protect endangered and threatened species. COSEWIC, a committee with scientific expertise, has been established to assess the status of wildlife species. SARA provides nine major legal levers for protecting listed species, including general prohibitions against harming species or damaging their residences. Part II highlights the sea of uncertainties being faced in implementation practice. Uncertainties include: contested listing criteria; politically dependent listing decisions; hazy general prohibitions; leeway for incidental harm permitting; recovery strategy and action plan fogginess; critical habitat issues; unsettled relationships with other federal laws; and methodological tensions in how risks should be managed. Part III seeks to chart a course for future legislative and institutional reforms. Besides amendments to SARA, the paper advocates the urgent need to move from “deathbed treatment” to proactive encouragement of biodiversity health through such initiatives as fully implementing Canada's Oceans Act, establishing a network of marine protected areas, and modernizing Canada's antiquated Fisheries Act.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.030 |
| Scholarly communication | 0.022 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".