A social network perspective on teammate interactions as cue to cohesion
Bibliographic record
Abstract
Early group dynamics theorizing suggested that member interactions form a structured network that can serve as cue for cohesiveness (Shaw, 1964). In terms of structure, research has found that the overall exchange of knowledge and information may serve as one of these cues to perceptions of cohesiveness, with greater exchange associated with greater task cohesiveness (McLaren & Spink, under review). Further, it has been speculated that a network that is less centralized and more dense (characterized by a greater proportion of connections between group members) would be associated with greater perceptions of cohesiveness (Shaw, 1964). Using social network analysis, these relationships between networks and cohesion were examined in two studies. Participants from intact teams (N = 205) in Study 1 identified team members with whom they regularly exchanged information (ego network) and reported perceived task cohesion. A discriminant function analysis was used to differentiate between the two groups (those who interacted with a greater proportion of teammates versus those who interacted with fewer) in terms of cohesion. Results revealed a significant difference, Wilks' Lambda = .87, p < .001. As predicted, those interacting with more teammates reported greater task cohesion than those interacting with less. Using an experimental vignette design, participants (N = 127) in Study 2 read one of two network team descriptions that varied in centrality and density. As expected, those who read about the team described with lower centrality/higher density reported higher task cohesion than those who read the higher centrality/lower density team description (p < .001).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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".