Signaling when no one is watching: A reputation heuristics account of outrage and punishment in one-shot anonymous interactions.
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Bibliographic record
Abstract
on Jul 22 2019 (see record 2019-43753-001). In the article, a printer error did not enable the authors to correct various errors before publication. Tables 1 and 2 have been corrected. All versions of this article have been corrected.] Moralistic punishment can confer reputation benefits by signaling trustworthiness to observers. However, why do people punish even when nobody is watching? We argue that people often rely on the heuristic that reputation is typically at stake, such that reputation concerns can shape moralistic outrage and punishment even in one-shot anonymous interactions. We then support this account using data from Amazon Mechanical Turk. In anonymous experiments, subjects (total n = 8,440) report more outrage in response to others' selfishness when they cannot signal their trustworthiness through direct prosociality (sharing with a third party)-such that if the interaction were not anonymous, punishment would have greater signaling value. Furthermore, mediation analyses suggest that sharing opportunities reduce outrage by influencing reputation concerns. Additionally, anonymous experiments measuring costly punishment (total n = 6,076) show the same pattern: subjects punish more when sharing is not possible. Moreover, and importantly, moderation analyses provide some evidence that sharing opportunities do not merely reduce outrage and punishment by inducing empathy toward selfishness or hypocrisy aversion among non-sharers. Finally, we support the specific role of heuristics by investigating individual differences in deliberateness. Less deliberative individuals (who typically rely more on heuristics) are more sensitive to sharing opportunities in our anonymous punishment experiments, but, critically, not in punishment experiments where reputation is at stake (total n = 3,422); and not in our anonymous outrage experiments (where condemning is costless). Together, our results suggest that when nobody is watching, reputation cues nonetheless can shape outrage and-among individuals who rely on heuristics-costly punishment. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it