When the <scp>SUIT</scp> Fits: Constructive Controversy Training in Face‐to‐Face and Virtual Teams
Bibliographic record
Abstract
Abstract One of the major reasons organizations have turned to work teams is because challenges are too complex, and too large in scope, for any single individual to address. As a result, teams must engage in information sharing, exchange, and processing that optimize the use of each team member's knowledge. Accordingly, we invoked a framework called SUIT, based on the theory of constructive controversy, that teaches teams to effectively share, understand, integrate, and make team decisions. We also considered whether a training program developed in accordance with the SUIT principles has stronger effects for virtual teams (VTs) relative to face‐to‐face (FtF) teams, given that VTs tend to need more information sharing and decision‐making support. Using a fully crossed and balanced experimental design, we found that teams receiving SUIT training reported greater constructive controversy levels and, in turn, higher objective task performance. The communication medium did not moderate this effect.
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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 | 0.002 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".