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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".