Sounding the alarm: Occurrences of fraud in nonprofit community sport organizations
Bibliographic record
Abstract
Abstract This study examines the prevalence of fraud occurrences in community sport organizations (CSOs) and compares the organizational characteristics of CSOs that have and have not experienced fraud. The empirical analysis relies on online survey data gathered in Canada, the United States, Australia, and Germany (n = 1256). Respondents were asked if organizational fraud had occurred in their CSO in the last ten years. In the full sample, 12.2% of organizations had experienced some type of fraud. The results showed occurrences of fraud were significantly higher among organizations that support the local community, have a high annual budget, possess grant income, and perform large and complex financial transactions and among those who lacked policies for handling assets and cash. In contrast, occurrences of fraud were significantly lower in organizations with a relatively small annual budget, a plan for the education and professional development of board members, and at least two individuals handling cash or checks. The analyses of geographic subsamples not only partially echoes the results for the full sample, but also shows further significant differences. The findings reveal that fraud occurrence across subsamples does not follow a clear pattern, demonstrating that prevention measures should be tailored based on geographic and organizational context.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.002 | 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".