Decision-Centered Testing (DCT): Evaluating Joint Human-Computer Cognitive Work
Bibliographic record
Abstract
In order to test the effectiveness of a human operator paired with a decision support system, it is necessary to complement current testing practices addressing software validation, human performance, and usability. Decision Centered Testing (DCT) aims at testing the effectiveness of operators teamed with Decision Support Systems (DSS) in any challenging work domain. DCT is grounded in a Cognitive Systems Engineering (CSE) framework, where the concept of a joint cognitive system (JCS) is central. DCT aims at evaluating the decision-making effectiveness across identified 'error prone' regions in the JCS structure. A description of the DCT Methodology with an illustration taken from an initial application of the methodology is presented. In this application, insights from the DCT methodology enabled the definition of appropriate test metrics and the construction of unique test scenarios to exercise the decision-making effectiveness. From this application, it can be concluded that following the DCT Methodology facilitated the construction of an evaluation framework for assessing JCS net decision-making effectiveness.
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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 | 0.018 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".