Religiosity as Self-Enhancement: A Meta-Analysis of the Relation Between Socially Desirable
Bibliographic record
Abstract
In a meta-analysis, the authors test the theoretical formulation that religiosity is a means for self-enhancement. The authors operationalized self-enhancement as socially desirable responding (SDR) and focused on three facets of religiosity: intrinsic, extrinsic, and religion-as-quest. Importantly, they assessed two moderators of the relation between SDR and religiosity. Macro-level culture reflected countries that varied in degree of religiosity (from high to low: United States, Canada, United Kingdom). Micro-level culture reflected U.S. universities high (Christian) versus low (secular) on religiosity. The results were generally consistent with the theoretical formulation. Both macro-level and micro-level culture moderated the relation between SDR and religiosity: This relation was more positive in samples that placed higher value on religiosity (United States> Canada> United Kingdom; Christian universities> secular universities). The evidence suggests that religiosity is partly in the service of self-enhancement. Keywords religiosity, self-enhancement, socially desirable responding, intrinsic religiosity, extrinsic religiosity People are motivated to see themselves favorably along cul-turally valued characteristics. Stated otherwise, people are motivated to self-enhance. This motive lies at the heart of many social psychological theories, such as cognitive dis-sonance theory, terror management theory, self-affirmation theory, social identity theory, the self-enhancement tactician model, and the self-evaluation maintenance model
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.025 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".