Interfaith prejudice in the United States: The role of social identity complexity
Bibliographic record
Abstract
Interfaith prejudice continues to be an understudied challenge in the United States. In three studies, the present research focused on prejudice between atheists and Christians and assessed the role that social identity complexity (SIC), the degree of overlap that an individual perceives between their various social identities, plays in these biases, as well as tested a novel intervention method using SIC to reduce bias. Study 1 assessed the relationship between SIC and prejudice for atheists and Christians and found that higher SIC in Christians was associated with lower levels of prejudice, but SIC was unrelated to prejudice for atheists. Study 2 tested the utility of an SIC intervention for reducing prejudice in Christians and showed a small increase in SIC but no reduction in prejudice towards atheists. A mini meta-analysis in Study 3 assessed the relationship between SIC and prejudice across all three studies and fully replicated Study 1. This line of work was an important first step in uncovering the relationship between SIC and interfaith intolerance for atheists and Christians in the United States. The findings suggest that SIC interventions may have potential for reducing prejudice in Christians, but future researchers will need to use methods that can be tested longitudinally for greater impact.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".