Bibliographic record
Abstract
How do people behave when they face a finite series of opportunities to cheat with little or no risk of detection? In 4 experiments and a small meta-analysis, we analyzed over 25,000 cheating opportunities faced by over 2,500 people. The results suggested that the odds of cheating are almost 3 times higher at the end of a series than earlier. Participants could cheat in 1 of 2 ways: They could lie about the outcome of a private coin flip to get a payoff that they would otherwise not receive (Studies 1-3) or they could overbill for their work (Study 4). We manipulated the number of cheating opportunities they expected but held the actual number of opportunities constant. The data showed that the likelihood of cheating and the extent of dishonesty were both greater when people believed that they were facing a last choice. Mediation analyses suggested that anticipatory regret about passing up a chance to enrich oneself drove this cheat-at-the-end effect. We found no support for alternative explanations based on the possibility that multiple cheating opportunities depleted people's self-control, eroded their moral standards, or made them feel that they had earned the right to cheat. The data also suggested that the cheat-at-the-end effect may be limited to relatively short series of cheating opportunities (i.e., n < 20). Our discussion addresses the psychological and behavioral dynamics of repeated ethical choices.
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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 | 0.037 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".