Explanations of and interventions against affective polarization cannot afford to ignore the power of ingroup norm perception
Bibliographic record
Abstract
Affective polarization, or animosity toward opposing political groups, is a fundamentally intergroup phenomenon. Yet, prevailing explanations of it and interventions against it have overlooked the power of ingroup norm perception. To illustrate this power, we begin with evidence from 3 studies which reveal that partisans' perception of their ingroup's norm of negative attitudes toward the outgroup is exaggerated and uniquely predicts their own polarization-related attitudes. Specifically, our original data show that in predicting affective polarization (i.e. how one feels about one's partisan outgroup), the variance explained by ingroup norm perception is 8.4 times the variance explained by outgroup meta-perception. Our reanalysis of existing data shows that in predicting support for partisan violence (i.e. how strongly one endorses and is willing to engage in partisan violence), ingroup norm perception explains 52% of the variance, whereas outgroup meta-perception explains 0%. Our pilot experiment shows that correcting ingroup norm perception can reduce affective polarization. We elucidate the theoretical underpinnings of the unique psychological power of ingroup norm perception and related ingroup processes. Building on these empirical and theoretical analyses, we propose approaches to designing and evaluating interventions that leverage ingroup norm perception to curb affective polarization. We specify critical boundary conditions that deserve prioritized attention in future intervention research. In sum, scientists and practitioners cannot afford to ignore the power of ingroup norm perception in explaining and curbing affective polarization.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".