Knowledge-based Support in a Group Decision Making Context: An Expert-Novice Comparison
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
This research examines the use of knowledge-based and explanation facilities to support group decision making of experts versus novices. Consistent with predictions from the persuasion literature, our results show that experts exhibit a higher level of criticality and involvement in their area of expertise; this not only decreases their likelihood of being persuaded by a knowledge-based system, but also accounts for a lower group consensus among experts as compared to novices. Novices are more easily persuaded by the system and find the system to be more useful than experts do. This research integrates theories from the persuasion literature to understand expert-novice differences in group decision making in a knowledge-based support environment. The findings suggest that the analyses and explanations provided by knowledge-based systems better support the decision making of novices than experts. Future research is needed to integrate other types of information provision support (e.g., cognitive feedback) into knowledge-based systems to increase their effectiveness as a group decision support tool for domain experts.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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