Perceiving acculturation from neutral and emotional faces.
Bibliographic record
Abstract
Facial expressions of emotion convey more than just emotional experience. Indeed, they can signal a person's social group memberships. For instance, extant research shows that nonverbal accents in emotion expression can reveal one's cultural affiliation (Marsh, Elfenbein, & Ambady, 2003). That work tested distinctions only between people belonging to one of two cultural categories, however (Japanese vs. Japanese Americans). What of people who identify with more than one culture? Here we tested whether nonverbal accents might signal not only cultural identification but also the degree of cultural identification (i.e., acculturation). Using neutral, happy, and angry photos of East Asian individuals varying in acculturation to Canada, we found that both Canadian and East Asian perceivers could accurately detect the targets' level of acculturation. Although perceivers used hairstyle cues when available, once we removed hair, accuracy was greatest for happy expressions-supporting the idea that nonverbal accents convey cultural identification. Finally, the intensity of targets' happiness related to both their self-reported and perceived acculturation, helping to explain perceivers' accuracy and aligning with research on cultural display rules and ideal affect. Thus, nonverbal accents appear to communicate cultural identification not only categorically, as previous work has shown, but also continuously. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".