Confronting risk: A case study of Aboriginal peoples' participation in environmental governance of uranium mining, Saskatchewan
Bibliographic record
Abstract
Abstract While the number of non‐regulatory environmental governance arrangements overseeing mining has been growing in Canada, there remains limited research critically examining Aboriginal peoples' experiences in these emerging institutions. This is especially notable with respect to the sharing of techno‐scientific information, often used as a means of enabling Aboriginal peoples' participation and engendering their trust. A qualitative case study of the Northern Saskatchewan Environmental Quality Committee oversight of uranium mining, this research explores how techno‐scientific information in the form of risk assessments was socially constructed and discursively employed by government and industry, and how Aboriginal participants responded. Findings illustrate that risk assessments were presented in ways that rendered development as controllable and inevitable, which facilitated dominant political economic agendas and capitalist practices. Aboriginal participants, however, introduced alternative interpretations of risk and sought to claim spaces within this governance institution through underscoring absent uncertainties, and asserting knowledges of global technological failures and local conditions that contradicted scientific reassurances. Aboriginal participants also highlighted the social injustices of development processes in Saskatchewan's north, which shaped their interpretations of risk, raising important questions about the value of these alternative governance institutions for Aboriginal peoples and their environments.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".