How Do Multiracial and Monoracial People Categorize Multiracial Faces?
Bibliographic record
Abstract
Due to their awareness of multiraciality and their perceptions of race categories as fluid, multiracial individuals may be unique in how they racially categorize multiracial faces. Yet race categorization research has largely overlooked how multiracial individuals categorize other mixed-race people. We therefore asked Asian, White, and multiracial individuals to categorize Asian-White faces using an open-ended response format, which more closely mirrors real-world race categorizations than forced-choice response formats. Our results showed that perceivers from all three racial groups tended to categorize Asian-White faces as monoracial Asian, White, or Hispanic. However, multiracial perceivers categorized the Asian-White faces as multiracial more often than monoracial perceivers did. Our findings suggest that multiracial individuals may approach racial categorization differently from either monoracial majority or minority group members. Furthermore, our results illustrate possible difficulties multiracial people may face when trying to identify other multiracial in-group members.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".