Discord Over DNA: Ideological Responses to Scientific Communication about Genes and Race
Bibliographic record
Abstract
Abstract The American public's beliefs about the causes of social inequality vary greatly, with debates over the causes of racial inequality tending to be the most salient and divisive. Among whites in particular, liberals tend to see inequality as rooted in society's ills, whereas conservatives tend to see inequality as rooted in individuals’ shortcomings. Given this, many infer that white conservatives are more likely than white liberals to adopt the controversial view that racial inequality is “natural,” i.e., due to genetically inherited characteristics. We argue that genetic explanations for racial inequality, in and of themselves, offer little appeal to white conservatives. However, when white citizens are exposed to media messages that emphasize the egalitarian implications of genetic similarity between racial groups, those on the left and right engage in biased assimilation, resulting in a “nature” (conservative) versus “nurture” (liberal) divide. Data from two studies of white Americans—one representative survey and one experiment—support this theoretical framework.
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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 | 0.003 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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; 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".