On the utility of experiential cross-training for team decision making under time stress
Bibliographic record
Abstract
This study investigated the effectiveness of experiential cross-training in a team context for team decision-making under time stress in a simulated naval surveillance task. It was hypothesized that teams whose members explicitly experience all team positions will perform better under time pressure due to a better shared Team Interaction Model (Cannon-Bowers et al. 1993). In addition, it was posited that experiential cross-training would reduce the negative effect of member reconfiguration that can occur in certain military situations. Three groups of teams participated in this study (cross-trained, reconfigured and control). The experiment involved three team training sessions, followed by three time-stressed exercise sessions. During training, one group of teams was cross-trained (CT) by asking each member to perform an entire session at each of the three team positions. Member reconfiguration (where each member was shifted to another's position) was unexpectedly introduced at the first of the exercise sessions for the CT group and for another group (reconfigured) that had not been cross-trained. A third (control) group was neither cross-trained nor reconfigured. During training, the performance of non-CT teams improved more quickly than that of CT teams. During the exercise, the CT group did not achieve the level of performance of the control teams. The immediate effect of team member reconfiguration was to degrade performance significantly for the non-CT teams, but not for CT teams. The findings are discussed in terms of the multiple mental models' view of team performance (Cannon-Bowers et al. 1993) and the authors discuss the relative utility of cross-training when overall training time is fixed.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".