Definition and constituents of maltreatment in sport: establishing a conceptual framework for research practitioners
Bibliographic record
Abstract
There has recently been an increased emergence of research on the maltreatment of athletes in sport. It is suggested that research may play a particularly salient role with respect to athlete protection initiatives. However, as it stands, current research in this area is limited by a lack of consistency in definitions. The purpose of the paper, therefore, is to propose a conceptual framework of maltreatment in sport to be used among research practitioners. More specifically, a conceptual model of the different categories, constructs and constituents of maltreatment in sport is proposed. Sport-specific examples of the various maltreatments are outlined. Current literature is reviewed, and recommendations are made for future research.
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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 | 0.021 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| 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".