Validating Methods in Cognitive Engineering: A Comparison of Two Work Domain Models
Bibliographic record
Abstract
Work domain analysis (WDA) is becoming a popular technique for the analysis of complex systems. WDA is one of the frameworks of Cognitive Work Analysis (CWA; Vicente, 1999) and can be used to gather work domain constraints as part of a user centered design process. In this paper, we discuss issues of inter-modeler reliability with WDA. The authors of this paper performed, over similar time periods, cognitive engineering analyses, including work domain analyses using abstraction hierarchy models, of two similar systems: naval combat vessels. In this paper, we compare these models for similarities and differences. Comparison indicated similarities in model scope and content, which would be an expected result of the application of a reliable modeling technique to two similar systems. Differences between the models included the use of multi-part vs. a single model to represent components of the overall ship-seacontact system, the related decisions to include sensors explicitly in the model, and the descriptions of abstract functions and constraints included in the two models. Exploration of these differences illuminated methodological as well as theoretical considerations in applying work domain modeling techniques that can provide guidance to other modelers.
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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 | 0.110 | 0.284 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".