For the good of the party, for the good of the nation: ingroup projection can motivate support for political violence
Bibliographic record
Abstract
Support for political violence in the United States appears to be growing. We outline a model that positions ingroup projection—seeing ingroup values as the shared values of a larger inclusive societal group—as a key psychological process through which identification with political ingroups (Democrats/Republicans) and with national superordinate categories (Americans) influence support for political violence. We tested this model in two studies. Study 1 ( N = 297) included original survey data collected during the 2016 US Presidential election, and analyses revealed that identification with Democrats or Republicans and identification with Americans both had positive indirect effects on support for political violence that were mediated by ingroup projection. Study 2 ( N = 642) provided a preregistered replication of these results using original data collected during the 2022 US Midterm Senate elections. These results provide insight into the psychological processes that underlie support for political violence in the United States and elucidate a generalizable model for understanding destructive intergroup behaviors in a variety of real-world intergroup contexts.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".