Come together: Effects of perceiving groupness on adherence in structured sport settings
Bibliographic record
Abstract
Being with others is not synonymous with being a group. Groups are as much about perceived social reality as physical reality (Campbell, 1958). Perceiving a collection as a group influences how individuals think and behave. In the exercise realm, the degree to which a collection of individuals was perceived to be a "group" was positively associated with member adherence in both structured and unstructured settings (Wilson et al., 2011). In sport, teams tend to vary in the characteristics that reflect groupness, like the tightness of bonds and degree of interaction among members. As such, one wonders whether perceiving the team as "groupier", even within the physical reality of being a "team", would positively relate to adherence in a manner similar to other structured activity settings. After self-identifying involvement in a structured sport team, participants (N = 166) completed an online questionnaire assessing groupness (i.e., common fate, mutual benefit, social structure, group processes, and self-categorization; Spink et al., 2010) and adherence (i.e., frequency and attendance). Structural equation modeling results revealed an acceptable model fit: ?2= 21.54, p = .06, RMSEA = 0.07 (CI: 0.00-0.11). Groupness was positively related to adherence, with the squared multiple correlation for adherence = .07. These findings support previous research in activity settings and suggest that groupness is an important variable to consider when assessing adherence in sport teams.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".