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
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it