The Still‐Face Effect: Methodological Issues and New Applications
Bibliographic record
Abstract
Over the last 25 years, the seemingly simple still‐face phenomenon has elicited a tremendous amount of empirical and theoretical work. Adamson and Frick (2003/this issue) provide a comprehensive review and in‐depth analysis of this large body of research. In our commentary, we focus on 3 major points. First, we described several methods to define operationally the still‐face effect. Second, we noted the important role of adult touch in the still‐face procedure, and that the effect can be reproduced without adult touch, by live, televised and “virtual” adult faces–‐making it a true “still‐face” effect. Third, we emphasized a major methodological strength of the still‐face procedure: the use of multiple response measures. By measuring both infant visual attention and affect responses, adaptations of the still‐face procedure provide infant researchers with a powerful general method for studying the development of infant social competence.
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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 | 0.244 | 0.445 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".