Descriptive analysis of university-student music preferences during different forms of physical activity
Bibliographic record
Abstract
Music can be used to enhance the pleasure derived from physical activity experiences through the selection of songs for each type and intensity of activity, which in turn can reduce negative physiological responses and improve adherence. Third- and fourth-year kinesiology undergraduate students completed a music questionnaire ( N = 113, 63 females). Questions pertaining to whether the individual uses music while participating in exercise and/or leisure activities were asked, in addition to specifics on the purpose of listening to music and their demographic information. The data showed significant individual differences in regard to music preferences for each type of activity. Participants preferred music with a fast tempo for aerobic exercise, slow tempo for strength-training exercise, and slow tempo often in a major mode for leisure type activities. Sex differences were minimal. The results reinforce the idea that individual differences in music choices is an important concept for practitioners and researchers to consider in their future work.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".