Advancing Our Understanding of Child Maltreatment and Safeguarding: Implications for Sport and Dance
Bibliographic record
Abstract
Abstract Public and scholarly recognition of the problem of child abuse and neglect in sport and dance contexts has grown substantially over recent years in response to the surge of testimonials of harms experienced by participants in these contexts. This recognition has led to a growing body of research that addresses the problem of maltreatment and has contributed to the proliferation of safeguarding initiatives to prevent and address maltreatment in children's sport and dance settings. However, given the criticisms of these initiatives and the recognition that childhood and child maltreatment are socially constructed, we argue that more nuanced, context-specific approaches to understanding experiences of maltreatment and safeguarding are needed. Drawing on theoretical frameworks developed in child protection and social work literature, we argue that more diverse approaches to safeguarding are needed, and that contextual specificity will influence the relevance and effectiveness of prevention and intervention measures. Further, the safe and authentic elicitation and implementation of children's perspectives is necessary to advance our understanding of maltreatment and the development of effective safeguarding measures. Future research and practice need to safely engage with the perspectives and ideas of the children who are at the centre of sport and dance safeguarding initiatives. Finally, we propose that the current focus on the prevention of harms in dance and sport organisations ignores other critical aspects of safeguarding including how we ensure that children experience optimal participation conditions to have the best possible outcomes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".