Knowledge-based Support in a Group Decision Making Context: An Expert-Novice Comparison
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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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".