Nunavik Inuit and Raglan Mine: New approaches to closure planning (<em>isulinnisanganut parnasimautiit</em>)
Bibliographic record
Abstract
This article explores an experimental approach to mine closure planning and community participation that foregrounds the dialogue between technical and legal approaches to the mine and the knowledge and values of affected Indigenous communities. Located in the Inuit territory of Nunavik in the Canadian province of Québec, the Raglan Mine is the site of a unique collaborative approach to mine closure planning. Although the Raglan Mine is expected to remain operational for at least another 20 years, the Closure Plan Subcommittee was launched in March 2018 to establish and maintain a dialogue with the mine's Inuit partners about mine closure. The objective is "to integrate the traditional knowledge of the communities, but also to exchange the scientific knowledge of the technical experts and the mine." Drafted in collaboration with members of the Subcommittee, this article reviews the regulatory context for mine closure planning in Nunavik, including the lack of requirements for community-engaged planning or integration of socio-economic objectives. It also reviews the key milestones of the Subcommittee's work to date and assesses progress towards its objective of establishing culturally relevant closure goals and criteria, and the integration of Inuit knowledge, enterprise, and industry know-how in the closure planning process.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.020 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".