French-speaking children’s freely produced labels for facial expressions
Bibliographic record
Abstract
In this study, we investigated the labeling of facial expressions in French-speaking children. The participants were 137 French-speaking children, between the ages of 5 and 11 years, recruited from three elementary schools in Ottawa, Ontario, Canada. The facial expressions included expressions of happiness, sadness, fear, surprise, anger, and disgust. Participants were shown one facial expression at a time, and asked to say what the stimulus person was feeling. Participants' responses were coded by two raters who made judgments concerning the specific emotion category in which the responses belonged. 5- and 6-year-olds were quite accurate in labeling facial expressions of happiness, anger, and sadness but far less accurate for facial expressions of fear, surprise, and disgust. An improvement in accuracy as a function of age was found for fear and surprise only. Labeling facial expressions of disgust proved to be very difficult for the children, even for the 11-year-olds. In order to examine the fit between the model proposed by Widen and Russell (2003) and our data, we looked at the number of participants who had the predicted response patterns. Overall, 88.52% of the participants did. Most of the participants used between 3 and 5 labels, with correspondence percentages varying between 80.00% and 100.00%. Our results suggest that the model proposed by Widen and Russell (2003) is not limited to English-speaking children, but also accounts for the sequence of emotion labeling in French-Canadian children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".