Impact of Prejudice on Ethnic Ingroup and Outgroup Mental Representations
Bibliographic record
Abstract
Dotsch et al. (2008) have shown that ethnic outgroup faces are perceived as less trustworthy and more criminal looking in the prejudiced mind. Since prejudice also involves favoritism towards the ingroup (Brewers, 1999), we hypothesized that not only the mental representations of outgroup faces are negatively biased in prejudiced individuals but also that their mental representations of ingroup individuals are positively biased. To reveal the prototypical representation of African-American (AA) and Caucasian (Ca) faces, we used Reverse Correlation (Mangini & Bierderman, 2004). On each trial, two stimuli (base face + and visual noise) were presented simultaneously on the screen, and the participant had to decide which one of the two was most typical of each ethnic group (i.e. AA or Ca). Thirty-seven participants underwent 500 trials for both ethnic representations, and completed an Implicit Association Test (IAT) to determine their level of prejudice against AA. Classification images (CI) representing prototypical AA or Ca faces were computed separately for each participant by averaging the noise patterns of the stimuli selected as most representative. Subsequently, twenty independent participants judged the level of trustworthiness, criminality, and successfulness displayed by the CI. For each judge and for each social judgment, a linear regression was performed on the IAT scores and the judges ratings of the CI. Subsequent t-tests on the regression coefficients showed that the more prejudiced a participant was, the less trustworthy [t(19)=4.44, p<.001] and potentially successful [t(19)=4.74, p<.001], and the more criminal looking [t(19)=-2.38, p<.05] their mental representations of AA were judged. Interestingly, the more prejudiced a participant was, the more trustworthy [t(19)=-4.02, p<.001] and potentially successful [t(19)=-4.17, p<.001] their representation of a Ca was judged. These results show that prejudice does not only bias the representation of the ethnic outgroup faces, but also those of the ingroup faces. Meeting abstract presented at VSS 2014
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".