Exploring the sport commitment of regional-level masters athletes as a function of gender and age
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 it