Rewards and Fear of Being Labeled as Racist: A Tax Fraud Whistleblowing Investigation*
Bibliographic record
Abstract
ABSTRACT This article investigates experimentally, in the income tax context, how whistleblowing intentions are influenced when a tax fraud perpetrator is of a different race than a potential whistleblower. In particular, it examines the impact of a message highlighting the social value of whistleblowing and how fear of being perceived as racist influences whistleblowing decision‐making. Using insights from the fear elicitation and processing literature, we find that a potential whistleblower from a majority racial group who learns about a fraud perpetrated by someone from a minority racial group is significantly more likely to blow the whistle anonymously when a social value message is present versus absent, as the presence of a social value message reduces fear of being perceived as racist. However, we do not find that race dissimilarity is significantly associated with non‐anonymous whistleblowing intentions, irrespective of the presence of a social value message. Furthermore, in non‐anonymous whistleblowing situations, potential whistleblowers have to disclose their identities to the tax authority to become eligible for a cash reward. Even when potential whistleblowers can choose the amount of a cash reward they would have to be paid in order to blow the whistle, our results show that whistleblowing intentions do not increase significantly when perpetrators and potential whistleblowers are of different races. Overall, our results suggest that the societal value of tax whistleblowing and the use of cash rewards for whistleblowing are limited by other sociological considerations.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".