Race, Religion, and Anti‐Poverty Policy Attitudes
Bibliographic record
Abstract
Using the 2008 National Politics Study, the present study indicates that while African Americans are more likely than whites to hear sermons about poverty and other political issues, hearing such sermons more consistently associates with support for anti‐poverty government programs among non‐Hispanic whites than among both African Americans and Hispanics. The racially/ethnically marginalized status of blacks and Hispanics may contribute to these groups being more receptive than whites to religious messages emphasizing social inequality. The contrasting racial experiences of dominance and marginalization may also help explain why hearing politicized sermons is more meaningful to the progressive social welfare attitudes of whites than to African Americans and Hispanics. This expectation is rooted in the heightened variability of perspectives among whites and their religious organizations regarding the government's role in aiding the economically disadvantaged. Conversely, the vast majority of blacks and Hispanics support the government helping individuals who fallen upon hard times. The greater variability in opinion among whites may also allow for greater differences in opinion to emerge between whites who attend relative to those outside of religious congregations led by clergy emphasizing spiritual and political solidarity with the poor than is the case for African Americans and Hispanics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".