The Contribution of Campus Recreational Sports Participation to Perceived Sense of Campus Community
Bibliographic record
Abstract
Out-of-class involvement provides students with opportunities for rich social lives which, according to Cheng (2004), are closely associated with sense of campus community. Based on Astin's (1984) Theory of Involvement, and Boyer's (1990) principles of community, the purpose of this study was to examine the degree to which involvement in campus recreational sports programs is associated with students' perceived sense of campus community. Three hundred and thirty respondents completed an on-line questionnaire which consisted of demographics and questions related to their out-of-class involvement in 14 areas as identified by the institutions' Dean of Students Office, and a 25-item sense of community scale developed by Cheng (2004). Exploratory factor analysis (EFA) was conducted to examine the underlying factor structure of the sense of community scale. The six factors extracted from the EFA served as independent variables in a multiple regression analysis used to predict student perceived sense of campus community using a sample of 125 participants in campus recreational sports. In addition, participation levels in campus recreational sports were used to measure differences in perceived sense of campus community based on involvement using a multivariate analysis of variance (MANOVA). Results suggest participation in campus recreational sports significantly predicted a sense of community within the diversity and acceptance factor. In addition, students who participated in campus recreational sports perceived a greater sense of campus community based on the residential experience factor when compared with those students who did not participate.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".