Prescriptive stereotypes and workplace consequences for East Asians in North America.
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 pursue the idea that racial stereotypes are not only descriptive, reflecting beliefs about how racial groups actually differ, but are prescriptive as well, reflecting beliefs about how racial groups should differ. Drawing on an analysis of the historic and current status of East Asians in North America, we study descriptive and prescriptive stereotypes of East Asians along the dimensions of competence, warmth, and dominance and examine workplace consequences of violating these stereotypes. Study 1 shows that East Asians are descriptively stereotyped as more competent, less warm, and less dominant than Whites. Study 2 shows that only the descriptive stereotype of East Asians as less dominant than Whites is also a prescriptive stereotype. Study 3 reveals that people dislike a dominant East Asian coworker compared to a nondominant East Asian or a dominant or a nondominant White coworker. Study 4 shows that East Asians who are dominant or warm are racially harassed at work more than nondominant East Asians and than dominant and nondominant employees of other racial identities. Implications for research and theory are discussed.
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 | 0.001 | 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