Toxic Legacies, Slow Violence, and Environmental Injustice at Giant Mine, Northwest Territories
Bibliographic record
Abstract
For fifty years (1949–99) the now-abandoned Giant Mine in Yellowknife emitted arsenic air and water pollution into the surrounding environment. Arsenic pollution from Giant Mine had particularly acute health impacts on the nearby Yellowknives Dene First Nation (YKDFN), who were reliant on local lakes, rivers, and streams for their drinking water, in addition to frequent use of local berries, garden produce, and medicine plants. Currently, the Canadian government is undertaking a remediation project at Giant Mine to clean up contaminated soils and tailings on the surface and contain 237,000 tonnes of arsenic dust that are stored underground at the Giant Mine. Using documentary sources and statements of Yellowknives Dene members before various public hearings on the arsenic issue, this paper examines the history of arsenic pollution at Giant Mine as a form of “slow violence,” a concept that reconfigures the arsenic issue not simply as a technical problem, but as a historical agent of colonial dispossession that alienated an Indigenous group from their traditional territory. The long-term storage of arsenic at the former mine site means the effects of this slow violence are not merely historical, but extend to the potentially far distant future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".