Prescriptive stereotypes and workplace consequences for East Asians in North America.
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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".