Normal Accident Theory versus High Reliability Theory: A resolution and call for an open systems view of accidents
Bibliographic record
Abstract
We resolve the longstanding debate between Normal Accident Theory (NAT) and High-Reliability Theory (HRT) by introducing a temporal dimension. Specifically, we explain that the two theories appear to diverge because they look at the accident phenomenon at different points of time. We, however, note that the debate’s resolution does not address the non-falsifiability problem that both NAT and HRT suffer from. Applying insights from the open systems perspective, we reframe NAT in a manner that helps the theory to address its non-falsifiability problem and factor in the role of humans in accidents. Finally, arguing that open systems theory can account for the conclusions reached by NAT and HRT, we proceed to offer pointers for future research to theoretically and empirically develop an open systems view of accidents.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".