Social Considerations in Mine Closure: Exploring Policy and Practice in Nunavik, Quebec
Bibliographic record
Abstract
Northern Canada has a long history of poorly remediated and outright abandoned mines. These sites have caused long-term environmental hazards, socio-economic disruptions, and threats to Indigenous communities across the North. Given the potential legacy effects of improper mine closure, best practice guidelines now suggest that mine closures address not only environmental remediation, but also include robust plans for mitigating social and economic impacts, and that companies engage early and consistently with impacted communities. This research seeks to understand how social and economic planning and community engagement for closure are governed in Nunavik, Quebec. Through semi-structured interviews with government and industry actors and an analysis of regional and provincial mining policy, this research demonstrates that mine closure regulations remain vague when describing how companies should involve impacted communities in mine closure planning, and governments largely neglect to regulate the social aspects of mine closure. This article discusses why an overreliance on impact assessment and overconfidence in closure regulations are creating risks for Nunavimmiut. Without regulatory change, future closures may continue to result in unemployment, social dislocation, costly abandoned sites, and continued distrust in the industry.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".