White racial identity, political attitudes, and selective exposure to information about racism
Bibliographic record
Abstract
Despite its documented benefits, information about racism can threaten White identity in ways that may motivate disengagement. In fact, people generally choose information that is favorable rather than unfavorable to their attitudes, social identities, or some aspect of the self (i.e., selective exposure). However, existing research is unclear about whether White racial identity can shape selective exposure, a question complicated by the fact that these identity-based motives might conflict with political attitudes that consistently shape information preferences. In eight studies (n = 3,080), we demonstrate that White racial identity–above and beyond racial and political attitudes–motivates selective exposure to information about racism. In Studies 1 and 2, White participants selected racism-irrelevant information over racism information—a pattern that distinguished them from racially minoritized participants even when accounting for racial and political attitudes. White participants in Studies 3a and 3b simultaneously considered how favorable information was to their racial identity and to their political attitudes when rating how threatening they expected information to be. These ratings almost perfectly mirrored patterns of selective exposure among White participants in Studies 4a-c. Across studies, identity-based motives helped to explain why White participants selected less information about racism. Study 5 further found that encouraging White participants to reappraise and seek discomfort (an affective state theorized to underlie selective exposure) led them to select more ideologically inconsistent information about racism, although the implication of this finding differs for White liberals and conservatives. These studies demonstrate the importance of both racial identity and political attitudes for understanding and addressing biases in how people process information about racism. We discuss these findings in terms of their implications for theories of selective exposure and for growing political conflicts over information about racism.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".