Reduction of Normalization of Deviation (NoD) Using a Socio-Technical Systems Approach
Bibliographic record
Abstract
Normalization of deviation (NoD), also known as normalization of deviance, is the process in which deviations from correct or proper decisions, behaviors, or conditions important for safety insidiously become the accepted norm over time. NoD is a common, risky, yet elusive issue causing or contributing to numerous accidents in multiple industries. Effective reduction of NoD is therefore a major opportunity. Approximately 10 years ago, Boeing developed a general systemic model of NoD based on a socio-technical systems approach. It is a representation of how multiple internal and external factors inherent to socio-technical systems interact in a dynamic fashion leading to NoD. It holistically captures the essence and complexity of the problem. The model has been shared across Boeing and with three customer airlines of Boeing. Specific systemic models of NoD associated with specific problems were developed based on the general systemic model. Subsequently, NoD awareness training, methods, tools, processes, and solutions based on those models have been developed. They were provided and/or used to improve workplace safety at Boeing and aviation safety at one of the three airlines. All the efforts have resulted in unprecedented insights, and some have seen significant reduction of NoD, NoD-related incidents, and NoD-related safety risks.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".