Bibliographic record
Abstract
This book addresses the major forms of Gender-Based Violence (GBV) in children’s sport, including sexual, physical, and psychological violence and neglect. It reviews the historical, sociocultural, and political influences on violence towards children, and sets out future agendas for research and practice to eliminate GBV in sport. The book argues that for GBV to occur and be sustained over time, it must be facilitated by a system that enables this violence, protects the perpetrator, disables bystanders, silences the victims, and/or fails to provide a structure by which to address victims’ or bystanders’ concerns. Drawing on empirical research from across a range of disciplines, including sport sociology, sport psychology, developmental psychology, and coaching, and examining real life case studies of GBV in sport at all levels, the book makes a powerful case for radical change in our current systems of sport governance, safeguarding, and athlete welfare. This is important reading for any student, researcher, policy-maker, coach, welfare officer or counsellor with an interest in sport, gender studies, safeguarding, criminology, or sociology. An electronic version of this book is freely available, thanks to the support of libraries working with Knowledge Unlatched (KU). KU is a collaborative initiative designed to make high quality books Open Access for the public good. The Open Access ISBN for this book is 9781003035138. More information about the initiative and links to the Open Access version can be found at www.knowledgeunlatched.org.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".