Ability of Children with Learning Disabilities and Children with Autism Spectrum Disorder to Recognize Feelings from Facial Expressions and Body Language
Bibliographic record
Abstract
In this study, children with learning disability (LD) were compared with children with autism spectrum disorder(ASD) in terms of identifying emotions from photographs with certain face and body expressions. The sampleconsisted of a total of 82 children aged 7-19 years living in Izmir in Turkey. A total of 6 separate sets of slides,consisting of black and white photographs, were used to assess participants’ ability to identify feelings – 3 sets forfacial expressions, and 3 sets for body language. There were 20 photographs on the face slides and 38 photographson the body language slides. The results of the nonparametric Mann Whitney-U test showed no significant differencebetween the total scores that children received from each of the face and body language slide sets. It was observedthat the children with LD usually looked at the whole photo, while the children with ASD focused especially aroundthe mouth to describe feelings. The results that were obtained were discussed in the context of the literature, andsuggestions were presented.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".