Linking board gender composition with fraud in community sport organizations: diversity is prevention
Bibliographic record
Abstract
Purpose This study examines the associations between past fraud occurrence, board gender diversity, trust, and fraud control in non-profit community sport organizations (CSOs).Methodology Data were collected from CSOs in Germany, Australia, and North America using an online survey (n = 1,256). Fraud control and team trust (including propensity to trust, trustworthiness, cooperative behavior) among board members were measured with established scales. Their mean indexes were used as dependent variables in seemingly unrelated regression models.Findings CSOs having experienced fraud in the past ten years are characterized by significantly lower levels of team trust overall, propensity to trust, trustworthiness, and cooperative behavior. While past fraud occurrence does not affect fraud control, board gender diversity is associated with more fraud control measures, but also lower levels of trustworthiness.Practical implications The present findings have implications for CSO governance in terms of trust versus control and how a gender diverse board can be a source of fraud prevention.Research contribution Linking board gender diversity theoretically and empirically with trust and fraud represents a contribution.Originality The study is based on unique primary data on fraud in CSOs allowing to study perceptions of trust and fraud control.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".