What does it take to stop a mine? Indigenous interventions and project rejection in Nunavut, Canada
Bibliographic record
Abstract
Indigenous groups who oppose extractive projects make use of a range of tactics and strategies, including intervening in impact assessment (IA) processes. This article examines four cases where IA authorities recommended against the development of proposed mines in Nunavut, Canada. By comparing these cases to the assessment of projects recommended for approval, we seek to draw broad conclusions about the different types of interventions that can potentially lead to project rejection. Based on an analysis of assessment and media documents, we found that Nunavut's IA Board recommended against project approval when Indigenous groups made technical, procedural, and political interventions within and alongside IA, when Indigenous rights-bearing organizations expressed substantial outstanding concern at the final hearing, and when there was already an operating mine in the region. These findings add nuance to critiques of IA as a tool for legitimizing extraction. In contexts like Nunavut – where IA processes substantially recognize Indigenous rights and mandate Indigenous representation – participation by rights-bearing groups may be more likely to influence IA outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".