Ethnic diversity and cohesion in interdependent sport teams
Bibliographic record
Abstract
Diversity is collective amount of differences among members within a social unit (Harrison & Sin, 2005, p. 196). In sport team contexts, the diversity of several member characteristics can have ramifications for group structure (e.g., roles), emergent states (e.g., cohesion), and processes (e.g., communication; Carron & Eys, 2012; McEwan & Beauchamp, 2014). One member attribute purported to be salient for groups is cultural background (Schinke et al., 2014). Although the effects of cultural diversity on group processes have been studied extensively in organizational psychology (e.g., Stahl et al., 2010), research in sport contexts is comparably rare. This study examined how member diversity (assessed via Blau's Index) of a typically overt aspect of culture, ethnicity (e.g., African-American, Asian, Indigenous, Hispanic), predicts team cohesion. Data were gathered from 203 intercollegiate athletes (87 males and 116 females) from 16 interdependent sport teams (e.g., basketball) at two time points (1-2 weeks apart). Controlling for cohesion at time 1, team size, average team tenure, and gender, team-level regression analyses revealed two task dimensions of cohesion at time 2 were positively predicted by in-group ethnic diversity at time 1: attractions to group-task, ? = 0.46, t(15) = 3.12, p =.011, ?R2 = 0.12, Overall model: Adjusted R2 = 0.81, F(1, 15) = 13.77, p
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".