Combating Serious Crime and Corruption in Sport: International and Comparative Perspectives Workshop
Bibliographic record
Abstract
This Briefing Paper details the actual and potential challenges, and impact, of gambling and corruption practices on sports in Australia. The paper draws on case studies from Australia and overseas, where the role of players, referees and agents, illustrate the pervasive attraction and negative impact of matchfixing. Vulnerabilities in organized sporting events are identified in this paper, ranging from individuals directly involved in a sport, to regulatory practices, and - perhaps the most challenging issue - the sophistication and complexity of betting avenues for gamblers, particularly the emergence of offshore platforms which offer lucrative avenues for the laundering of money and perpetration of fraud. This Briefing Paper reviews current strategies in Australia and overseas to regulate and promote integrity in sport - by sporting bodies, gambling operators and governments - and highlights important considerations that should be taken into account in these approaches. The Briefing Paper concludes by noting the importance of academic research in enhanced responses to gambling-led corruption, and the promotion of integrity in sport.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".