Team communication networks, task cohesion, and performance: A case study
Bibliographic record
Abstract
Through the lens of team cognition (Cooke, 2015), recent field and experimental research supports early group dynamics theorizing that communication network structure may serve as a cue to perceived cohesion (McLaren & Spink, 2017). Specifically, a communication network that is lower in centrality and higher in density (based on information and knowledge exchange between team members) offered a coordinated cognitive system where members feel like they were on the same page (i.e., cohesive) and the team had a greater probability of success. Using a case study approach, the current study analyzed two soccer teams, who differed in overall league success, and competed in a game. Communication networks (players identified the members they exchanged information with during the game) and perceptions of task cohesiveness were assessed. Team A (n = 13), the second-ranked team (7-1-1) in the six-team league, won the game (4-0) over Team B (n = 13), the fifth-ranked team (2-7-0). Based on past research (McLaren & Spink, 2017), it was hypothesized that the more successful team (generally and current outcome) would have the more coordinated network structure and report higher perceptions of task cohesion. As hypothesized, Team A (the more successful team) presented a more coordinated network structure (i.e., lower centrality, greater density) that also included shorter distances between members and more members in the core of the network versus the periphery when compared with Team B. Along with this more coordinated communication network structure, athletes on Team A also reported greater perceptions of task cohesion.Acknowledgments: Social Science and Humanities Research Council of Canada Doctoral Scholarship to the first author (752-2014-2655)
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".