Bibliographic record
Abstract
Since the mid 1980’s Canada’s plans for nuclear fuel waste (NFW) management, and the authority and knowledge of the nuclear industry have been brought into question. One of the most significant contemporary challenges to the narratives and claims of the nuclear industry about the safety of NFW, its effects and its management, is the experience of Aboriginal peoples, such as the Serpent River First Nation (SRFN), with different parts of the nuclear fuel chain. This paper interrogates the means through which the nuclear industry (through the work of the newly formed Nuclear Waste Management Organization) maintains control over the production of knowledge about NFW and contains and redirects the challenges to their accounts presented by Aboriginal peoples. I identify a discourse of ‘modern risk’ as instrumental to the industry’s success, and using insights from recent scholarship on scale and power, examine the relationship cast between the knowledge of the nuclear industry and of the SRFN. I argue that the discourse of modern risk is a scalar discourse that normalizes the claims of the nuclear industry and disqualifies those of the Serpent River First Nation by scaling knowledge.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.038 | 0.049 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 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".