Extirpation despite regulation? Environmental assessment and caribou
Bibliographic record
Abstract
Abstract Many caribou populations in Canada face extirpation despite dozens of provincial and federal legislative instruments designed to protect them. How are industrial developments that impact caribou justified and permitted despite governments' commitments to caribou protection? Toward an answer, this paper scrutinizes an approval process for major projects in Canada: environmental assessment (EA). We identify 65 EAs for major projects with potentially significant adverse impacts for caribou—all projects but one were approved. The results show that most projects were approved on the basis of proposed mitigation measures that promise to render adverse effects “insignificant”; yet mitigation effectiveness is largely unknown. Further, several projects were approved even though mitigation measures were insufficient, citing public or national interest. Finally, some projects' approval rested in part on scientific claims that the project area is already degraded or absent of caribou. Based on these findings, EA is failing caribou, acting as a means by which the state licenses major developments with potentially significant adverse effects for caribou, with a pretense of protection. The failure stems in part from a broader tension within the state that manifests in EA: a tension between the state's roles promoting economic growth and protecting against this growth's negative effects. Recognition of this tension needs to be more central to conservation biology.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".