The effect of instructions and response format on smile judgement.
Bibliographic record
Abstract
Our study examined the role of instructions, response type, and definition on the judgement of enjoyment and nonenjoyment smiles. Participants viewed symmetric Duchenne, non-Duchenne, and asymmetric smiles. They were instructed to judge the happiness, authenticity, and sincerity of the smiles using either Likert scales or a dichotomous response type. Participants were also either given a definition of the instruction words "happy," "authentic," and "sincere" or not. Results showed that the probability of saying "really (happy/sincere/authentic)" was higher for the symmetric Duchenne than the asymmetric smiles and higher for the asymmetric than non-Duchenne smiles. Changing the instructions given to participants did not override the effect of smile type with the use of Likert scale or dichotomous response. However, with the use of Likert scale, we observed subtilities that were not observed with the use of dichotomous response. When given a definition, in the case of symmetric non-Duchenne smiles, Likert ratings were significantly lower, and participants were more accurate in their judgement on the dichotomous scale. However, no differences were observed for the asymmetric Duchenne and symmetric Duchenne smiles whether a definition was given or not. Symmetric non-Duchenne and asymmetric Duchenne smiles were also viewed longer when a definition was given than when one was not. Nevertheless, considering methodological variations of our study failed to explain the variations in the pattern of results of previous studies, other avenues should be explored, such as the use of dynamic stimuli and a greater variety of encoders. (PsycInfo Database Record (c) 2023 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 | 0.024 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".