Bibliographic record
Abstract
Physical abuse refers to the repeated infliction of physical harm (Perry et al., 2002) or non-accidental physical injury (Matthews, 2004). In sport, physical harm may result from such practices as excessive or inappropriate stretching, the use of excessive exercise as punishment, or requiring athletes to hold uncomfortable positions for extended periods of time. Given the physical nature of sport, it is surprising to see so little research on physical abuse of athletes; this may be attributed to the normalization of such practices. Neglect - a form of athlete maltreatment - refers to a lack of reasonable care, deficits in meeting a young person's basic needs, and an all-round deprivation of attention and nurturing (Crooks & Wolfe, 2007; Glaser, 2002; Iwaniec, 2003). Of all forms of maltreatment, neglect has been the least researched in both the general child development (Miller-Perrin & Perrin, 2007) and in sport contexts (Stirling, 2009). Forms of neglect that a sport psychology consultant may become aware of include such examples as denying an athlete water during training, playing an injured athlete against medical advice, or the controlling of social relationships outside of sport. In addition to needing further sport psychology research into physical abuse and neglect, practicing consultants would benefit from such knowledge and understanding their role in prevention and intervention. Narratives of athletes' experiences of physical punishment and neglect will be presented.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".