Political partisanship influences perception of biracial candidates' skin tone
Bibliographic record
Abstract
People tend to view members of their own political group more positively than members of a competing political group. In this article, we demonstrate that political partisanship influences people's visual representations of a biracial political candidate's skin tone. In three studies, participants rated the representativeness of photographs of a hypothetical (Study 1) or real (Barack Obama; Studies 2 and 3) biracial political candidate. Unbeknownst to participants, some of the photographs had been altered to make the candidate's skin tone either lighter or darker than it was in the original photograph. Participants whose partisanship matched that of the candidate they were evaluating consistently rated the lightened photographs as more representative of the candidate than the darkened photographs, whereas participants whose partisanship did not match that of the candidate showed the opposite pattern. For evaluations of Barack Obama, the extent to which people rated lightened photographs as representative of him was positively correlated with their stated voting intentions and reported voting behavior in the 2008 Presidential election. This effect persisted when controlling for political ideology and racial attitudes. These results suggest that people's visual representations of others are related to their own preexisting beliefs and to the decisions they make in a consequential context.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".