Bibliographic record
Abstract
Canada's siting process for spent nuclear fuel, led by the Nuclear Waste Management Organization (NWMO), is frequently held within nuclear industry spheres as an exemplary siting process, designed to be inclusive, participatory, and “community-driven.” Drawing from ethnographic observations of the process as it unfolded in Southern Ontario, Canada, this paper focuses on the epistemic issues of how diverse knowledges are treated in the process, whose knowledge is valued, how such knowledges are understood, and whose knowledges are excluded. In particular, I make sense of how epistemic tensions in the process are produced by being situated within a nuclear landscape, informed by local nuclear-dominant socio-technical relations and epistemic regimes, which exceptionalize pro-nuclear Western scientific knowledges. This socio-technical constellation, I suggest, leads to careful but sometimes paradoxical negotiations of the expert/lay divide that subsequently reveals cracks in the policy foundation for inclusion of diverse forms of knowledge. While the NWMO policy framework discursively values diverse knowledges, critical lay community knowledges are often delegitimized and dismissed. Similarly, there are scalar issues in the ways Indigenous knowledges are homogenized and devalued through discursive separation. These epistemic tensions, between how knowledges should be treated in policy, and how knowledges are actually treated in practice, demonstrate clear issues of recognition justice, participatory fairness, and inclusion of diverse knowledges. The implications of this work shed light on understanding the complexities of landscape-based knowledge politics and how they might inform siting practices and technological decision-making more broadly.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.047 | 0.113 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| 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".