Ideological Differences in Race and Gender Stereotyping
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
We investigated whether political ideology was associated with the endorsement of race and gender stereotypes, and examined motivational and cognitive factors that could account for any ideological differences. Across five preregistered studies, people who were more politically conservative more strongly supported the use of stereotypes to make social inferences based on race, and endorsed specific stereotypes about racial and gender groups. An internal meta-analysis indicated that a greater desire to uphold group-based hierarchy and lower epistemic motivation to deliberate explained, in part, why conservatives were more likely to endorse the use of stereotypes, while cognitive ability did not have a significant explanatory role. These findings suggest that characteristics of individuals not inherently linked to any particular social group can shape perceptions about whether stereotypes are valid, and highlight how basic psychological motivations lead liberals and conservatives to diverge in their perceptions of groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 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