Examining the relationship between individual and team level communication network structure and task cohesion and team performance across time
Bibliographic record
Abstract
It has been reported recently that athletes who (a) exchanged information with a larger proportion of their teammates (i.e., higher outdegree centrality) and (b) were a member of a team who, as a whole, engaged in a higher proportion of information exchange (i.e., higher network density) also perceived their team to be more task cohesive. The current study extended these findings to a sample of intact sport teams and tested the exchange/task cohesion relationship across time (i.e., prospective design). In addition, objective team performance was added as a second outcome variable as it has a putative association with information exchange network structure in other group settings. Controlling for early season perceptions of task cohesion, a multilevel regression analysis revealed that individual outdegree centrality significantly predicted midseason task cohesion perceptions (R2change = .26, p < .001). The overall variance accounted for in this multilevel model was captured at both the individual (41%) and team (19%) level. In the second analysis, a hierarchical regression controlling for early season team performance found that network density significantly predicted midseason team performance (R2change = .08, p < .001). These results highlight a pattern of relationships between information exchange and both task cohesion and team performance consistent with past theorizing. In terms of uniqueness, it also was found that specific aspects of information exchange (i.e., individual versus team level network structure) differed for each dependent variable, which has not been reported previously.Acknowledgments: Social Sciences and Humanities Research Council of Canada
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".