Sports and Secondary Crime Prevention: Youth at Risk
Bibliographic record
Abstract
At the secondary level of crime prevention, sport-based programmes have been relied upon to reach and support youth deemed ‘at risk’. As discussed earlier, sport participation is mistakenly assumed to reform at-risk youth and prevent them from criminal involvement (Coakley, 2011; Eckholm, 2013; Riley et al, 2017). There is some evidence that sport can be an effective tool for recruiting and delivering other crime prevention interventions to mitigate risk factors and strengthen protective factors of crime and violence (Cameron and MacDougall, 2000; United Nations, 2020). Many youth crime or drug prevention programmes use sports as a vehicle or platform for delivering various other forms of interventions. They are usually designed as early interventions to reduce the impact of risk factors and enhance corresponding protective factors, by targeting ‘high risk’ individuals or groups (Kelly, 2012a; 2012b). The nature and impact of these other interventions is sometimes unclear. The programmes may have many benefits for participants, but they tend to overstate their ability to prevent crime (Kelly, 2012b). The ‘evidence’ of their success, when there is any at all, is mostly anecdotal or based on the perceptions of participants or programme managers. Furthermore, many community-based programmes with limited funding focus on receptive youths and overemphasize the fact that these youth may somehow be ‘at risk’. Some of the programmes focus primarily on drug prevention. However, as Crabbe observed, sport is used in drug prevention and treatment interventions because young people enjoy it, but it is for the same reason that they might also choose to use illicit drugs or engage in criminal activity or sport-related violence (Crabbe, 2000). Moreover, the whole approach seems oblivious of the problem of doping and the use of performance enhancing drugs. Spruit and her colleagues (2018b) evaluated a Dutch sportbased programme for youth at risk for juvenile delinquency. The primary outcome was juvenile delinquency, measured by official police data. The secondary outcomes were risk and protective factors for delinquency, assessed with selfand teacher reports. The study found small but significant intervention effects on juvenile delinquency, and no effects on the risk and protective factors of juvenile delinquency (Spruit et al, 2018b, 2018b).
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".