Sport Participation and Primary Crime Prevention
Bibliographic record
Abstract
The sport environment is obviously a popular and important training ground for child and adolescent development. Sports stand with several other activities (for example, education, mentoring, religious teaching, and volunteering) as a potential factor in influencing positive social development among children and youth. Crime prevention strategies have therefore tried to build on the popularity and benefits of sports activities to promote youth development and to influence risk and resiliency factors associated with criminal involvement. A wide variety of community-based programmes aim to use sport either as a means or as a complementary activity to promote youth development and prevent youth crime. Because of its presumed ability to contribute to moral development (Pennington, 2017), character building and the acquisition of life skills, sport (especially competitive team sport) is frequently promoted as having the potential to contribute to crime prevention or the reduction of antisocial behaviour (Coalter, 2007, 2012). However, these primary-level crime prevention initiatives are seldom explicit about the type of crime they purport to prevent (Groombridge, 2017). Some of them refer generally to deviant or problem behaviour and may include anything from lack of self-discipline, defiance of authority, school absenteeism, or experimentation with drugs, to theft, violent and confrontational behaviour, or contacts with the police. Other initiatives specifically refer to violent behaviour, delinquency, or gang involvement, without specifying the exact behavioural nature of the outcomes to be achieved. There is a great deal of wishful thinking and proselytizing behind many programmes (Giulianotti, 2004). Despite numerous positive anecdotal accounts, there is still little evidence to support the assumption that sport participation is effective in reducing youth crime (Coakley, 1998). Methodological and practical challenges in evaluating the impact of sport participation explain why there is little definitive/ empirical evidence to support the assumption that sport is effective in reducing youth crime. Challenges include identifying what aspects to measure to gauge success, poor-quality data, and difficulties in isolating the impact of sport-based initiatives from other confounding factors. Due to research limitations, it is difficult to reach general conclusions on the effectiveness of these programmes (Public Safety Canada, 2017).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 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".