Attitudes on Policy and Punishment: Opposition to Inequality-Based Government Aid Predicts Support for Capital Punishment
Bibliographic record
Abstract
Objective: There exists a well-developed body of research on the attitudinal correlates of support for capital punishment. Among the most robust of these is racism and racial attributions. The study presented here was designed to explore whether policy prescriptions reflective of racial attitudes can predict support for capital punishment. Method: Data come from the 2018 iteration of the NORC General Social Survey. The dependent variable is a dichotomous measure of support for the death penalty for people convicted of murder. The independent variable is a 5-level Likert-type item of support for government aid to Blacks to help overcome discrimination. Binary logistic regression was used to analyze the relationship between variables net of standard controls. Results: Over 63 percent of the total sample supported the death penalty. Support among those strongly favored government aid to Blacks was 41 percent. Support among those who strongly rejected aid to Blacks was 78 percent. Results of the regression analysis showed each decrease in the level of support for government aid to Blacks was associated with an 18.6 percent increase in the likelihood of supporting the death penalty. Conclusion: Capital punishment support is not simply a function of abstract, hypothetical racial attitudes. The findings reported here suggest support for the death penalty is associated with concrete policy prescriptions that maintain racial inequalities. Given that capital punishment continues in large part due to public support, it should be recognized that this support is based on a desire to maintain racial inequalities through government action.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".