Bibliographic record
Abstract
Abstract Rasmussen and Vicente’s cognitive work analysis (CWA) is well known as an approach to developing a rich set of design requirements. CWA has become quite well recognized as an approach to understand complex domains and generate requirements for effective new designs. These requirements have resulted in information system interfaces that have improved performance in process control, health, finance, and military domains. The pattern of performance improvements seen with displays developed from CWA is quite particular. For example, improved performance is often seen in fault detection and diagnosis, but not particularly in the performance of regular tasks. Human performance in unanticipated situations is improved, but not performance in normal situations. One way to look at the effects of CWA-based interventions is to consider that CWA creates performance more typical of experts. CWA was a method founded on attempts to understand human expertise and transfer the knowledge of human experts into a design so that the less expert could benefit. From this grounding, CWA is an important method for understanding and transferring expertise. This chapter will move through the steps of CWA and their various contributions to the understanding and development of expertise. Finally, how CWA can be used to develop and transfer expertise through design will be discussed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.008 |
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".