Reduction of Normalization of Deviation (NoD) Using a Socio-Technical Systems Approach
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it