Judgements of Smiles in an Intergroup Context
Bibliographic record
Abstract
Most previous research has reported an ingroup advantage for emotion recognition accuracy (e.g. Elfenbein & Ambady, 2002b; Friesen et al., 2019). On the other hand, when researching discrimination between deliberate and spontaneous smiles in a minimal group paradigm, Young (2017) found an outgroup advantage. The goal of the present study was to investigate this phenomenon and examine its underlying mechanisms. Participants completed two blocks of trials where they rated the genuineness of deliberate and spontaneous smiles, as well as reported their motivation to attend to members of either group. The results showed no significant advantage for ingroup or outgroup in smile discrimination accuracy, nor did overall accuracy change with the inclusion of group information. However, participants showed a significantly laxer threshold for labelling a smile as genuine when evaluating ingroup members. Motivation to attend to individuals was highest for ingroup members. These findings suggest the presence of an ingroup positivity bias in ratings of smile genuineness within a minimal group paradigm. Moreover, they suggest that intergroup biases in emotion recognition VI may be less pronounced than previously thought and highlight the potential role of mediators in shaping these biases.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".