Lessons From a Comparison of Work Domain Models: Representational Choices and Their Implications
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
As methods in cognitive work analysis become more widely applied, questions regarding the impact of modeling choices and similarities in modeling efforts across projects and domains are increasingly relevant. However, no explicit comparison of models of similar systems has been reported. This paper compares independently developed work domain analysis (WDA) models of two command and control environments. Similarities in model content and the types of nodes included provide evidence that WDA techniques can capture fundamental elements regarding purposes and constraints. These points of agreement provide a common starting point for developing work domain representations of military command and control systems. The comparison also revealed differences between the models. Although differences in content reflected differences in scope of coverage and level of detail, other differences corresponded to more fundamental choices in modeling approach. These included the treatment of sensors, level of integration in the model, and representation of particular abstract constraints. Examination of these more fundamental differences pointed to important degrees of freedom in how to represent a WDA and clarified the implications of these modeling choices for guiding design. Actual or potential applications of this research include aiding analysts in making work domain modeling choices as well as producing work domain models of command and control environments.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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