Three facial expressions mothers direct to their infants
Bibliographic record
Abstract
Abstract Mothers modify both their voices and their faces when interacting with infants. Although considerable work has detailed the modifications in the voice, less is known about those in the face. In this paper, three specific types of infant‐directed (ID) facial expressions were identified in videotapes of 10 English‐ and 10 Chinese‐speaking mothers interacting with infants aged 4–7 months. Four measures were taken to examine the form and meaning of these ID facial expressions. In Measure one, 32 undergraduates easily differentiated the three identified facial expression types. In Measure two, the muscle movement of each type were described through Ekman and Friesen's facial action coding system (FACS). In Measure three, 35 mothers and 40 undergraduates provided different emotional descriptions and communicative messages for each type. In Measure four, rank correlations were conducted to identify the FACS units most indicative of each facial expression type. These four measures confirmed the appearance of three expression types in both Chinese and English mothers, the involvement of unique muscle movements in these expression types in comparison to adult‐directed expressions which have been described, and the expression of distinct and consistent emotional messages. The meaning and importance of these expressions to mother‐infant interactions are discussed, and directions for future research are identified. Copyright © 2003 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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 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".