Asian = machine, Black = animal? The racial asymmetry of dehumanization.
Bibliographic record
Abstract
How different racial minorities experience racism differently remains underexplored in existing research. Here, we show that Asian and Black people are often dehumanized differently. Twelve studies spotlight a racial asymmetry in dehumanization using a wide array of methods (experimental, archival, and computational) and data sources (online samples, word embeddings, and U.S. Bureau of Labor Statistics data): Whereas Black people are more often subjected to animalistic dehumanization, Asian people are predominantly subjected to mechanistic dehumanization. We demonstrate this asymmetry from the vantage point of victims (Studies 1a and 1b) and perpetrators (Studies 2a-2d). We further document the prevalence of this asymmetry across diverse domains, from everyday language (Study 3) to perceptions in the realms of romantic relationships (Study 4a), crime rates (Study 4b), and business skills (Study 4c). Finally, we demonstrate the asymmetry's real-world consequences in labor market segregation (Studies 5 and 6). Our findings shed light on the distinct experiences of racism encountered by different racial groups and, more critically, introduce a framework that unifies and integrates scattered empirical observations on perceptions of Asian people. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".