Leisure-Time Sport among Physical Education Students: A Time Trend Analysis of Sport Participation Styles
Bibliographic record
Abstract
The aim of this study is to uncover a time trend analysis of styles in active sport participation. Basic styles of sport participation are selected and compared from a time trend perspective, covering a 27-year period. The data were collected via questionnaire from different samples of male and female first-year university students in physical education. Sport participation data from 1972 (males only), 1976 (females)/1977 (males), 1982, 1988/93 and 1998/99 are considered and compared. Principal components analysis was used to explore basic dimensions of sport participation at each time interval. The 1972–82 style analysis of the males’ sport participation resulted in a triptych of styles consisting of competitive/traditional, local recreational, and conspicuous recreational sport activities. For the girls, the 1976–82 period yielded a competitive/traditional versus an ostentatiously recreational style pattern. Between 1982 and 1999 active sport participation as a leisure activity did not basically further diverge into different styles of participation. Rather, participation in the different modes of sport participation seems to have intensified. The results are interpreted in a broader approach of style analysis in leisure research and from the perspective of societal changes with regard to sports. It is concluded that style analysis may offer a valuable contribution to research on active sport participation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".