Narrow prototypes of Asian subgroups in the United States: Implications for the Stop Asian Hate movement
Bibliographic record
Abstract
The Stop Asian Hate movement is a collective for several anti-Asian-violence rallies and organizations in the United States (US). Research indicates that when asked to think about who is Asian, Americans' prototype primarily comprises East Asian individuals (e.g., people from China, Japan, Korea) at the exclusion of people from other regions of Asia (e.g., South Asia). The current work extends this prototypicality research to examine implications for social justice movements. We focused on the Stop Asian Hate movement, which was designed to raise awareness and protest racial discrimination directed towards Asian Americans, particularly in light of COVID-19. Three studies tested whether people's prototypes regarding who is Asian influenced who they believe is represented by the Stop Asian Hate movement, as well as potential implications of this bias. Compared to South Asians, people judged East Asians as more represented by the Stop Asian Hate movement (Study 1). When described as being the victim of a hate crime, participants perceived East Asian targets to be more credible, more traumatized, and their reporting of the crime on the SAAPI website was deemed more appropriate, compared to South Asian targets (Studies 2-3), effects that were mediated by judgments of prototypicality (Study 3).
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".