The plurality effect: People are more dishonest toward group than individual targets
Bibliographic record
Abstract
Prior research on the relationship between group versus individual targets and unethical behavior directed toward those targets is incomplete. Extending this line of research, the present paper examines whether individuals engage in more dishonest behaviors when interacting with a group (vs. an individual). Across six experiments and three supplemental studies ( N = 2376), we found that individuals demonstrated more dishonesty toward groups as opposed to individual targets, which we label the plurality effect . This effect was observed across a variety of situations (both low-stakes and high-stakes contexts with real monetary payouts), including when providing advice to others with an incentive to be dishonest, in employment interviews, and in negotiations. Mediation tests revealed that participants experienced lower moral concern when the target was a group versus an individual, and this finding held after testing for alternative explanations. Group membership and collectivism jointly moderated the effect, such that the plurality effect was stronger for targets who are members of the decision makers outgroup (vs. ingroup) among decision makers with high (vs. low) collectivistic values.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".