Scientific and Social Judgments of Safety in the Nuclear Fuel Waste Management and Disposal Concept
Bibliographic record
Abstract
In February 1998, the environmental assessment panel for Atomic Energy of Canada's (AECL) nuclear fuel waste management and disposal concept issued its report, in which it concluded that the idea of burying nuclear waste deep in the Canadian Shield is not acceptable as it stands, because it has not been demonstrated to have broad public support.1 The report offers an intriguing analysis of the concept of safety, according to which there are two perspectives one can take to evaluate whether a project is safe, a technical perspective and a social perspective. According to this analysis, the proposal for burying nuclear waste in the Canadian Shield could be judged to be safe from one perspective, but not from the other. In fact, the panel concluded that burying nuclear waste in rock is safe from a technical point of view, but that it has not been shown to be safe from a social point of view, for which reason it found the project to be unaccept able at this time. In this paper I explore the analysis of the concept of safety that led the panel to adopt this curious conclusion. I argue that the panel's analysis represents an advance over many such discussions, because it legitimizes the understanding of the concept of safety held by the general public, which is based on the idea of protection from harm. However, I conclude that the analysis ultimately fails because it does not
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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 | 0.044 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.013 | 0.093 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.007 |
| 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".