Black and White, or Shades of Gray? Racial Labeling of Barack Obama Predicts Implicit Race Perception
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present research capitalized on the prominence and multiracial heritage of U.S. 2008 presidential election candidate Barack Obama to examine whether individual differences in classifying him as Black or as multiracial corresponded to differences in implicit perception of race. This research used a newly developed task ( Sedlins, Malahy, & Shoda, 2010 ) with digitally morphed mixed‐race faces to assess implicit race perception. Participants completed this task four times before and one time after the election. We found that people who labeled Obama as Black implicitly perceived race as more categorical than those who labeled Obama as multiracial. This finding adds to the growing literature on multiracial perception by demonstrating a relationship between the explicit use of multiracial and monoracial race classification and implicit race perception. The results suggest potential implications for governmental, educational, and judiciary usage of racial categories .
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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 it