Bibliographic record
Abstract
Six studies explored the overlap between racial and gender stereotypes, and the consequences of this overlap for interracial dating, leadership selection, and athletic participation. Two initial studies captured the explicit and implicit gender content of racial stereotypes: Compared with the White stereotype, the Asian stereotype was more feminine, whereas the Black stereotype was more masculine. Study 3 found that heterosexual White men had a romantic preference for Asians over Blacks and that heterosexual White women had a romantic preference for Blacks over Asians; preferences for masculinity versus femininity mediated participants' attraction to Blacks relative to Asians. The pattern of romantic preferences observed in Study 3 was replicated in Study 4, an analysis of the data on interracial marriages from the 2000 U.S. Census. Study 5 showed that Blacks were more likely and Asians less likely than Whites to be selected for a masculine leadership position. In Study 6, an analysis of college athletics showed that Blacks were more heavily represented in more masculine sports, relative to Asians. These studies demonstrate that the gender content of racial stereotypes has important real-world consequences.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".