Rethinking remediation: mine closure and community engagement at the Giant Mine, Yellowknife, Northwest Territories, Canada
Bibliographic record
Abstract
Mine remediation entails long-term risks due to the need to contain and monitor \ndangerous materials. To date, research on mine remediation in Canada has focused \nprimarily on technical fixes; little is known about the political and social nature of \nremediation. Using the Giant Mine in Yellowknife, NWT as a case study, this thesis \nanalyzes mine remediation in the Canadian sub-Arctic and investigates how local \ncommunities shape remediation processes. Applying the concepts of ecological \nrestoration, environmental justice, social waste theory, and theories of repair, and care, \nthis thesis analyzes how effectively community concerns have been included in \nremediation planning. This thesis asks: how can the current approach to mine remediation \nbe changed from a focus on site containment to a broader emphasis on community \nremediation, restoration, and reconciliation? Without a community objectives based \napproach to remediation, such projects risk continuing systems of colonization, \nmarginalization and environmental degradation.
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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 | 0.003 | 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 | 0.038 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| 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".