When and why is religious attendance associated with antigay bias and gay rights opposition? A justification-suppression model approach.
Bibliographic record
Abstract
Even in relatively tolerant countries, antigay bias remains socially divisive, despite being widely viewed as violating social norms of tolerance. From a Justification-Suppression Model (JSM) framework, social norms may generally suppress antigay bias in tolerant countries, yet be "released" by religious justifications among those who resist gay rights progress. Across large, nationally representative US samples (Study 1) and international samples (Study 2, representing a total of 97 different countries), over 215,000 participants, and various indicators of antigay bias (e.g., dislike, moral condemnation, opposing gay rights), individual differences in religious attendance was uniquely associated with greater antigay bias, over and above religious fundamentalism, political ideology, and religious denomination. Moreover, in 4 of 6 multilevel models, religious attendance was associated with antigay bias in countries with greater gay rights recognition, but was unrelated to antigay bias in countries with lower gay rights recognition (Study 2). In Study 3, Google searches for a religious justification ("love the sinner hate the sin") coincided temporally with gay-rights relevant searches. In U.S. (Study 4) and Canadian (Study 5) samples, much of the association between religious attendance and antigay bias was explained by "sinner-sin" religious justification, with religious attendance not associated with antigay bias when respondents reported relatively low familiarity with this justification (Study 5). These findings suggest that social divisions on homosexuality in relatively tolerant social contexts may be in large part due to religious justifications for antigay bias (consistent with the JSM), with important implications for decreasing bias. (PsycINFO Database Record
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".