An Investigation of the Influence of Guilt, Awards, and a Moral Message on Tax Whistleblowing Decisions
Bibliographic record
Abstract
Abstract We examine experimentally the extent to which three potential tax authority interventions encourage the reporting of tax fraud to tax authorities and how two types of guilt feelings are involved in this decision. Using a sample of 728 adult taxpayers in the United States, we find that a cash award, a prosocial award and a moral suasion message positively influence whistleblowing intentions and that the moral suasion effect is mediated by intrapsychic guilt (when an individual violates their moral values) and interpersonal guilt (when one's actions cause harm to another). The combination of a cash award and moral suasion message results in the greatest likelihood of tax whistleblowing. Our research contributes to the tax whistleblowing literature by providing evidence of the efficacy of potential interventions and also extends literature on the role of moral emotions by showing the relevance of intrapsychic and interpersonal guilt to the tax fraud reporting decision.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".