The role of criminal justice in enhancing punitive measures for sports-related offenses: a multivariate comparative study
Bibliographic record
Abstract
Introduction: This study explores criminal activities in sports—specifically doping, match-fixing, and violence—emphasizing the need for strong legal frameworks, enforcement, and societal backing to uphold sports integrity. Objective: To assess how legal systems, penalties, and institutional coordination in various countries impact the prevention and management of sports-related offenses, using a comparative legal analysis. Methodology: Seven countries—Italy, Germany, China, the UK, USA, Canada, and Iraq—were selected based on diversity in legal systems, sports development levels, and data availability. The study analyzed national laws, WADA guidelines, and international reports. Countries were classified into proactive-punitive, sports-centric, moderate, or reactive-minimal systems. Socio-cultural and institutional legitimacy factors were included alongside legal norms. Results: Lower recidivism and higher public trust were found in systems with clear laws and effective coordination. Weak legal frameworks led to repeated offenses and reintegration challenges. Preventive and educational efforts significantly reduced repeat offenses across all country types. Discussion: Vague laws and fragmented institutions undermine sanction effectiveness. In contrast, coherent rules and policies support both deterrence and rehabilitation. Cultural trust and institutional legitimacy often outweigh the severity of penalties in influencing outcomes. Conclusion: Effective collaboration and legal clarity enhance responses to sports-related crimes. The global sports sector should adopt unified standards, with comprehensive strategies—combining punitive, preventive, and educational approaches—proving most effective in preserving integrity and reducing criminal behaviors.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".