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Record W2109904891 · doi:10.1177/0018726709339117

Normal Accident Theory versus High Reliability Theory: A resolution and call for an open systems view of accidents

2009· article· en· W2109904891 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHuman Relations · 2009
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsFalsifiabilityAccident (philosophy)Cognitive reframingPerspective (graphical)Reliability (semiconductor)Computer scienceDimension (graph theory)EpistemologyPsychologySocial psychologyArtificial intelligenceMathematicsPhilosophy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.161
GPT teacher head0.513
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it