Anti-Asian discrimination: how perceptions differ and why
Bibliographic record
Abstract
This study assesses five theoretical perspectives explaining why perceptions of anti-Asian discrimination vary: experiential, cultural, motivational, ideological, and identificational. Utilizing data from the U.S. and Canada (the Asian Canadians’ Experiences Survey (2023) and the ANES-GSS 2020 Joint Study), we find empirical support for each of the theoretical perspectives. Perception of discrimination against Asians can be experiential, reflecting one’s personal experiences of racial discrimination. Cultural factors such as the interpretation of what counts as discrimination and differential racialization also shape perceptions of anti-Asian discrimination. Among White individuals, perception of anti-Asian discrimination can be motivational and driven by feelings of social distance or threat from immigrants or Asians. The perception of anti-Asian discrimination also reflects ideological differences. Furthermore, Asians with a more salient racial identity report increased perceptions of discrimination. While our study focuses on anti-Asian discrimination, these explanations also help illuminate perceived discrimination against other racial minority groups.
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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 | 0.000 | 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 | 0.000 |
| 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".