In-Group Loyalty and the Punishment of Corruption
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study suggests that in-group loyalty, defined as the degree to which people favor their own group over others, undermines the punishment of corruption. We present evidence from two studies. First, we utilize a real-world corruption scandal involving the ruling party in Spain that broke during survey fieldwork. People exposed to the scandal withhold support from the incumbent, but in-group loyalty based on partisanship weakens this effect. Second, we explore in-group loyalty beyond partisanship through laboratory experiments. These experiments artificially induce group identities, randomly assign the group identity of candidates and shut down any instrumental benefits of in-group loyalty. The experimental evidence suggests that people support corrupt candidates as long as they share a group identity and are willing to sacrifice material payoffs to do so. Our findings have important implications. Most importantly perhaps, they suggest that candidates can get away with corruption by engaging in identity politics.
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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 | 0.000 | 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 | 0.007 |
| 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