Evidence of Structure-Specific Teamwork Requirements and Implications for Team Design
Bibliographic record
Abstract
This article reports an experiment using the C 3 Fire microworld—a functional simulation of command and control in a complex and dynamic environment—in which 24 three-person teams were organized according to either a functional or multifunctional allocation of roles. We proposed a quantitative approach for estimating teamwork requirements and comparing them across team structures. Two multiple linear regression models were derived from the experimental data, one for each team structure. Both models provided excellent fits to the data. The regression coefficients revealed key similarities and some major differences across team structures. The two most important predictors were monitoring effectiveness and coordination effectiveness regardless of team structure. Communication frequency was a positive predictor of performance in the functional structure but a negative predictor in the multifunctional structure. In regard to communication content, the proportion of goal-oriented communications was found to be a positive predictor of team performance in functional teams and a weak negative predictor of team performance in multifunctional teams. Mental load was a useful predictor in functional teams but not in multifunctional teams. Results show that this method is useful for estimating teamwork requirements and support the claim that teamwork requirements can vary as a function of team structure.
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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 | 0.005 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".