Beyond remediation: Containing, confronting and caring for the Giant Mine Monster
Bibliographic record
Abstract
Mine remediation entails long-term risks associated with the containment and monitoring of dangerous materials. To date, research on mine remediation in Canada has focused primarily on technical fixes; little is known about the socio-political and colonial aspects of remediation. Using the Giant Mine in Yellowknife (Northwest Territories, Canada) as a case study, this research investigates the story of the Giant Mine ‘Monster’, how it was defined, how it has changed and how nearby communities will care for the mine in the future. Using a mixed-methods approach, this research combines literature reviews, archival analysis, key informant interviews and participant observation in analyzing the multiple experiences, practices and stories of the Giant Mine Remediation Project. Directed by the frameworks of ecological restoration, Indigenous environmental justice and science and technology studies theories of care, this research reveals that, by focusing on the technical containment of arsenic trioxide pollution, the Giant Mine Remediation Project sidelined community objectives for compensation, independent oversight and a perpetual care plan. However, through the ongoing activism of the Yellowknives Dene First Nations and community allies, the Giant Mine Monster is being creatively reframed as something to care for and live with for generations to come – a responsibility for mining wastes that settlers across Canada have yet to meaningfully reckon with. I argue that the Giant Mine case points to a critical reconceptualization of environmental remediation as an anti-colonial mechanism to (re)structure, or (re)mediate, relationships with both land and people. Without a community objectives based approach to remediation, such projects risk continuing systems of colonization, marginalization and environmental injustice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.045 | 0.051 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".