Putting Cognitive Work Analysis to Work in Industry Practice: Integration with ISO13407 on Human-Centered Design
Bibliographic record
Abstract
This paper investigated how to conduct concrete design for industrial systems to conform to the recently-established Human-Centered Design standard ISO13407 (ISO). Referring to Sanderson et al.'s (1999) System Life Cycle (SLC) research, we adopted the Cognitive Work Analysis (CWA) framework as one useful and concrete designing method for industrial systems design to conform to ISO. Based on this idea, we compared three approaches ISO, SLC, and CWA and surveyed the match between ISO and CWA. From this study, we learned that integrating this research would provide great benefits to expand and improve the ISO concept for industrial systems, to give SLC a concrete methodological perspective, and to facilitate CWA technology transfer to practical design. As a result of these benefits, designers for industrial systems can get more structured ways of conducting adequate designs to help workers adapt to any demands and also to conform to ISO. An industry case study with a design example of a pump plant system supported our ideas. Also, we could confirm that much of the required information for ISO could be extracted by CWA. Therefore, the CWA models would not only be useful tools for industrial system designers in all of the SLC stages, but they also would be helpful to make our designs conform with the ISO standard.
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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 | 0.044 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".