Exploring the sport commitment of regional-level masters athletes as a function of gender and age
Bibliographic record
Abstract
The Sport Commitment Model (SCM; Scanlan et al., 2003) examines motivations behind continued sport participation. Using a modified SCM (Wilson et al., 2004), we surveyed 193 regional/provincial level Ontario adults from mixed sport types (99 m, 94 f; M age = 51.5). Two separate multiple regressions showed that enjoyment (? = .45) and personal investments (.31) predicted functional commitment (FC; R2 = .55), whereas social constraints (? = .34) and personal investments (.32) predicted obligatory commitment (OC; R2 = .22), ps< .001. Regarding gender, females reported higher enjoyment and FC levels, ps < .006. A series of regression analyses to examine gender effects revealed no differences for predictors of FC, with enjoyment (? m = .51; f = .26) and personal investments (m = .35; f = .54) significant. Personal investments (m = .39; f = .26) and social constraints (m = .26; f = .46) predicted OC for both, whereas enjoyment (-.22) and involvement alternatives (-.25) uniquely predicted female and male levels, respectively, ps < .04. Exploration of differences across young (35-44), middle (45-54) and older (55+ yr) age groups showed no significant mean differences for predictors, FC or OC. Regression analyses showed similar results for FC across all groups, with enjoyment and personal investments significant predictors, ps < .05. The OC model did not fit the 55+ group (p = .15). Social constraints (? y= .54; mid = .26), personal investments (y= .34; mid = .44), and enjoyment (y = -.34; mid = -.31) each predicted OC for the two younger groups, whereas social support (-.29) and involvement opportunities (.28) were predictors only for 35–44 yr-olds, ps
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".