Crime Prevention Outcomes and Implications for Future Investments
Bibliographic record
Abstract
This chapter concludes the book with a discussion of the place of sports in overall crime prevention strategies and whether further investments in sport-based crime prevention programmes are justified. The coincidence of popular interest in sport-based crime prevention programmes and the limited empirical evidence on their impacts underscores the need for further research. At this point, although there is useful knowledge about what can increase the positive development aspects of a sport-based programme, it remains very difficult to know for whom sport-based crime prevention interventions are most effective and what they should consist of. Definitive conclusions are still not possible about what, if anything, can make sport-based interventions more effective in preventing crime or violence (Spruit et al, 2018a). That drawback points to the critical need for substantial and rigorous evaluations. There is still a need to isolate and understand both the protective and the negative influences of sports on youth crime and to plan interventions so that negative influences can be confined or mitigated (Spruit et al, 2016). It stands to reason that ‘[t] he most successful sport-related programmes and projects are those which understand what is possible and clearly articulate and implement what they are trying to achieve. Resources should not be allocated to projects which make unqualified claims relating to their capacity to impact upon specific social outcomes. (Crabbe et al, 2006: 4) Sport-based crime prevention programmes are rarely evaluated and, when they are, the evaluations are methodologically weak and overly simplistic in their theorizing about the causes of youth crime (Bailey, 2005). Based on the present review, it appears that none of the sport-based crime prevention programmes in British Columbia have been evaluated or empirically reviewed and none have formulated a clear theory of change or logic model that directly links activities to crime prevention. It was not even clear that any of these programmes were committed to achieving specific or measurable crime prevention outcomes, and programme leaders did not appear particularly interested in participating in an evaluation of their programme. This is not unique to British Columbia. Yet, one should still ask whether the available evidence of their crime prevention effectiveness justifies investments of crime prevention funds, and whether investments in other programmes may not yield far more important results.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.003 |
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".