How Americans Evaluate Redistributive vs. Symbolic Racial Justice Policies
Bibliographic record
Abstract
Abstract Recent debates over how to address racial injustice in the United States often center on two types of policies: redistributive measures that redress material inequities between groups and symbolic reforms that challenge dominant racial narratives. How do citizens evaluate these differing approaches to advancing racial justice? How do recent removals of Confederate symbols shape support for each of these policy types? In a survey of American adults, we find that support for redistributive and symbolic policies is positively correlated across partisan, racial, and regional lines. However, when pressed, respondents express a stronger preference for redistributive measures, often viewing symbolic reforms as insufficient or distracting. In an experimental framework, we find that informing respondents about recent Confederate statue removals does not significantly alter support for either policy type. Looking at qualitative reactions to the treatment, we identify a plausible explanation for this null finding: most respondents see the removals as a fight over history and less directly relevant to a broader racial justice policy agenda.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".