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Record W3202684658 · doi:10.15439/2021f91

Design and application of facial expression analysis system in empathy ability of children with autism spectrum disorder

2021· article· en· W3202684658 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnals of Computer Science and Information Systems · 2021
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsEmpathyAutism spectrum disorderFacial expressionAutismPsychologyExpression (computer science)Facial expression recognitionDevelopmental psychologyIntervention (counseling)Cognitive psychologyFacial recognition systemComputer scienceSocial psychologyCommunicationPsychiatryPattern recognition (psychology)

Abstract

fetched live from OpenAlex

Empathy is an important social ability in the early childhood development. One of the significant characteristics of children with autism spectrum disorder (ASD) is their lack of empathy, which makes it difficult for them to feel and understand other people's emotions and to judge other people's behavioral intentions, leading to social disorders. This research designs and implements a facial expression analysis system that could obtain and analyze the real-time facial expressions of children when viewing stimulus materials, and then evaluate the differences of empathy ability between ASD children and typical development (TD) children. The results of this research provide new ideas for the evaluation of ASD children, and also help to develop empathy intervention plans for ASD 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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.283
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it