Bridging the Gap Between Cognitive Work Analysis and Ecological Interface Design
Bibliographic record
Abstract
The Cognitive Work Analysis and Ecological Interface Design frameworks have garnered a great deal of attention in recent years. The former is used to analyze a complex work domain to identify the behavior-shaping constraints imposed by the work domain, control tasks, strategies, operator competencies, and socio-organizational factors. The latter informs the design of operator interfaces for complex systems. Although the two frameworks overlap, a gap remains between the analysis and design stages. This article shows one path across that gap. Aspects of both frameworks were applied to the design of an ecological interface for a petrochemical process. We discuss how the project was completed under realistic time and budget constraints, review several unanticipated obstacles that we encountered, and relate design examples to help overcome the gap between analysis and design.
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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 | 0.029 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".