Punishment strategies in sport: The use of physical conditioning
Bibliographic record
Abstract
This study examined the use of physical conditioning as a form of punishment in sport. Although physical conditioning exercises are often used to improve physical fitness, anecdotal reports suggest that they may also be used to ensure social control of athletes and their behaviour. To date, there is an absence of empirical research in sport examining the use of physical conditioning exercises as a form of punishment. A descriptive survey was created for the purposes of this study. A total of 335 female and male kinesiology and physical education students completed the survey; 45 different sports were represented. The participants’ highest level of sport participation were: international (6%), national (CIS) (17%), provincial (18%), club (45%), and intramural/houseleague (12.5%). Overall, 88% of the participants reportedly engaged in physical conditioning as punishment with the most common forms being: (i) continuous running, swimming, cycling, or skating (66%); and, (ii) weight lifting, chin-ups, push-ups, or sit-ups (41%). The most common reasons for incurring punishment were: being late, poor attitude, lack of effort, poor practice and poor competition performances. No gender differences existed in any of the analyses. Participants' reportedly experienced fatigue (84.5%), decreased enthusiasm (39%), irritability (37.5%), apathy (18%) as a result of experiencing conditioning as punishment. A Pearson chi-squared test indicated that as the level of competition increased, there was a significant (p=.01) decrease in the experience of punishment increased. The findings are interpreted through theories of power and social control. Recommendations for future research and applied interventions will be presented.
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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".