Resistance to Nuclear Waste Disposal: Credentialed Experts, Public Opposition and their Shared Lines of Critique
Bibliographic record
Abstract
This article asks the question whether, in regard to controversial technical decision-making, lay public groups advance different kinds of resistance than credentialed experts. This question is explored via a case-study analysis of one of Canada's major public controversies of the past quarter century—nuclear waste disposal. Having arrived on the policy radar in 1977, nuclear waste remained an internal government/nuclear industry matter until terms of reference for a public inquiry were announced in 1989. Several access points for public input followed that announcement: scoping sessions in 1990, comments received during 1994-96 on an Environmental Impact Statement (EIS) prepared by Atomic Energy Canada Limited (AECL), nation-wide public hearings in 1996-97, and ongoing public consultation since 2002. This article focuses on the comments on the EIS, and discusses several lines of shared resistance: the expert judgment of AECL was disputed, the lack of peer review was criticized, accusations of unreliability were made, and general deficiencies in the EIS were attributed to narrow terms of reference and poor institutional culture. This article recommends the use of a dramaturgical approach to technical texts, and reveals the assumptions framing the dualist notion that one can unambiguously separate technical and social criticisms of technical projects.
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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 | 0.102 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.030 | 0.092 |
| Scholarly communication | 0.031 | 0.012 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.025 | 0.016 |
| 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".