Can Environmental Assessment Protect Caribou? Analysis of EA in Nunavut, Canada, 1999-2019
Bibliographic record
Abstract
Abstract This paper analyses the environmental assessment of every proposed mining project that has undergone full review through the Nunavut Impact Review Board from 1999 to 2019, with specific emphasis on how impacts to caribou were identified and assessed. Caribou are the most important terrestrial species in Nunavut from a food security, traditional culture, and harvesting perspective, and mining is known to have impacts on caribou habitat, migration and calving behaviour, predation and hunting patterns, and other effects. Close study of how caribou impacts are discerned and evaluated within environmental assessment (EA) can thus reveal broader trends about both EA and the broader resource governance process. Although some project proposals were initially rejected, every EA ultimately concluded that impacts to caribou were not significant, despite evidence presented to the contrary. We present three modes through which serious impacts are rendered insignificant within EA (mitigation, strategic use of scale, and strategic use of Inuit knowledge and consultation) and comment on the broader context shaping EA in Nunavut. We argue that EA cannot do what it is expected to do (come to rational, science-based decisions that balance ecological, social, and economic goals) and is an insufficient tool for ensuring the long-term well-being of caribou in Nunavut.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".