Human Factors Experiences in Context - Comparing Four Industrial Cases Using a Soft Systems Framework
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
In industrial production companies, the practice of assigning responsibility for human factors and ergonomics (HFE) to specific professionals (referred to as HF agents in this paper) may take on various organizational forms. This interview study examines the extent to which HF agents are able to give input towards the design of new production systems in different industrial sectors. The present paper reports on how HF agents work in four Canadian case companies from the Automotive, Nuclear Power, Poultry and Auto parts sectors. A stratified soft-systems framework was used to guide the comparison of the four case companies regarding the HF agents' positioning in their companies and how this influences their work practices. HF agents and a cluster of 2 -3 surrounding colleagues with adjacent responsibilities were interviewed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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