How do involvement opportunities vary according to sample segment in Masters swimmers
Bibliographic record
Abstract
Involvement opportunities (IOs) are perceived benefits that are only present through continued sport involvement (Weiss & Amorose, 2008). Knowing which IOs are poignant in different segments of a population may be important in explaining participants’ sport commitment, their behaviours, and purchase intentions (Casper & Stellino, 2008; Young, Bennett & Séguin, 2014). This study examined how 724 Masters swimmers judged IOs, as a function of age group (25-39, 40-54, 55-69, 70+), sex, prior participation length (< 5, 5+ yrs), and probability (low, high) of attending a World championship event. Participants reported information on demographics, sport involvement, intentions, and responded to a survey (Bennett & Young, 2013) assessing 10 different IOs. A series of MANOVAs identified differences according to sample segments, all ps < .007. All age cohorts highly recognized opportunities for ‘enjoyment’, ‘health and fitness’, ‘social’, ‘stress relief’ and ‘personal testing and assessment’, though the youngest group viewed the latter two IOs most highly. Each consecutively older group acknowledged ‘delaying/negotiating aging’ with increasing importance, and ‘team attachment’ with decreasing importance. Males more highly reported ‘personal testing and assessment’ and ‘recognition for competitive achievements’, whereas females more highly acknowledged ‘enjoyment’, ‘stress relief’, ‘health and fitness’, ‘social’, and ‘team attachment’. Short-term swimmers judged ‘personal testing and assessment’ and ‘team attachment’ more highly, whereas long-term swimmers rated ‘delaying/negotiating aging’ and ‘travel’ highly. Swimmers indicating probable attendance at an open World championship were higher on ‘personal testing and assessment’, ‘team attachment’, ‘recognition for competitive achievements’, and ‘travel’. Findings suggest that Masters swimmers recognize special opportunities arising from sport, which they risk losing should they quit. The salience of certain IOs depends on the participatory segment. Discussion focuses on how sport programmers, managers, and Masters event marketers might increase the effectiveness of their communication activities by strategically matching content in their promotional messages to segment-specific IOs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".