Mining sick: Creatively unsettling normative narratives about industry, environment, extraction, and the health geographies of rural, remote, northern, and Indigenous communities in British Columbia
Bibliographic record
Abstract
Rural, remote, northern, and Indigenous communities on Turtle Island are routinely—as Cree Elder Willie Ermine says—pathologized. Social science and health scholarship, including scholarship by geographers, often constructs Indigenous human and physical geographies as unhealthy, diseased, vulnerable, and undergoing extraction. These constructions are not inaccurate: peoples and places beyond urban metropoles on Turtle Island live with higher burdens of poor health; Indigenous peoples face systemic violence and racism in colonial landscapes; rural, remote, northern, and Indigenous geographies are sites of industrial incursions; and many rural and remote geographies remain challenging for diverse Indigenous peoples. What, however, are the consequences of imagining and constructing people and places as “sick”? Constructions of “sick” geographies fulfill and extend settler (often European white) colonial narratives about othered geographies. Rural, remote, northern, and Indigenous geographies are discursively “mined” for narratives of sickness. This mining upholds a sense of health and wellness in southern, urban, Euro‐white‐settler imaginations. Drawing from multi‐year, relationship‐based, cross‐disciplinary qualitative community‐informed experiences, and anchored in feminist, anti‐colonial, and anti‐racist methodologies that guided creative and humanities‐informed stories, this paper concludes with different stories. It unsettles settler‐colonial powers reliant on constructing narratives about sickness in others and consequently reframes conversations about Indigenous well‐being and the environment .
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.039 | 0.051 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".