How information about race-based health disparities affects policy preferences: Evidence from a survey experiment about the COVID-19 pandemic in the United States
Bibliographic record
Abstract
In this article, we report on the results of an experimental study to estimate the effects of delivering information about racial disparities in COVID-19-related death rates. On the one hand, we find that such information led to increased perception of risk among those Black respondents who lacked prior knowledge; and to increased support for a more concerted public health response among those White respondents who expressed favorable views towards Blacks at baseline. On the other hand, for Whites with colder views towards Blacks, the informational treatment had the opposite effect: it led to decreased risk perception and to lower levels of support for an aggressive response. Our findings highlight that well-intentioned public health campaigns spotlighting disparities might have adverse side effects and those ought to be considered as part of a broader strategy. The study contributes to a larger scholarly literature on the challenges of making and implementing social policy in racially-divided societies.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".