Methodological Variations to Explore Conflicting Results in the Existing Literature of Masking Smile Judgment
Bibliographic record
Abstract
Although a smile can serve as an expression of genuine happiness, it can also be generated to conceal negative emotions. The traces of negative emotion present in these types of smiles can produce micro-expressions, subtle movements of the facial muscles manifested in the upper or lower half of the face. Studies examining the judgment of smiles masking negative emotions have mostly employed dichotomous rating measures, while also assuming that dichotomous categorization of a smile as happy or not is synonymous with judgments of the smile's authenticity. The aim of the two studies was to explore the judgment of enjoyment and masking smiles using unipolar and bipolar continuous rating measures and examine differences in the judgment when instruction varied between judgments of happiness and authenticity. In Experiment 1, participants rated smiles on 7-point scales on perceived happiness and authenticity. In Experiment 2, participants rated the smiles on bipolar 7-point scales between happiness and a negative emotion label. In both studies, similar patterns were observed: faces with traces of fear were rated significantly less happy/authentic and those with traces of anger in the brows were rated significantly happier/more authentic. Regarding varied instruction type, no effect was found for the two instruction types, indicating that participants perceive and judge enjoyment and masking smiles similarly according to these two instructions. Additionally, the use of bipolar scales with dimensions between a negative emotion label and happiness were not consistently effective in influencing the judgement of the masking smile.
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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 | 0.060 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".