Perceiving acculturation from neutral and emotional 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
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).
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) | 0.005 | 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