Two Faces of the Other-Race Effect: Recognition and Categorisation of Caucasian and Chinese Faces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The other-race effect is a collection of phenomena whereby faces of one's own race are processed differently from those of other races. Previous studies have revealed a paradoxical mirror pattern of an own-race advantage in face recognition and an other-race advantage in race-based categorisation. With a well-controlled design, we compared recognition and categorisation of own-race and other-race faces in both Caucasian and Chinese participants. Compared with own-race faces, other-race faces were less accurately and more slowly recognised, whereas they were more rapidly categorised by race. The mirror pattern was confirmed by a unique negative correlation between the two effects in terms of reaction time with a hierarchical regression analysis. This finding suggests an antagonistic interaction between the processing of face identity and that of face category, and a common underlying processing mechanism.
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.
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 | 0.000 | 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 it