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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".