Empires built on sand: On the fundamental implausibility of reactor safety assessments and the implications for nuclear regulation
Bibliographic record
Abstract
Abstract This paper explores the nature of expert knowledge‐claims made about catastrophic reactor accidents and the processes through which they are produced. Using the contested approval of the AP1000 reactor by the US Nuclear Regulatory Commission (NRC) as a case study and drawing on insights from the Science and Technology Studies (STS) literature, it finds that the epistemological foundations of safety assessments are counterintuitively distinct from most engineering endeavors. As a result, it argues, those assessments (and thus their authority) are widely misconstrued by publics and policymakers. This misconstrual, it concludes, has far‐reaching implications for nuclear policy, and it outlines how scholars, policymakers, and others might build on a revised understanding of expert reactor assessments to differently frame, and address, a range of questions pertaining to the risks and governance of atomic energy.
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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 | 0.048 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.093 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 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".