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Record W3039023610 · doi:10.1177/0145445520939856

Effects of Video Modeling on the Acquisition, Maintenance, and Generalization of Playing with Imaginary Objects in Children with Autism Spectrum Disorder

2020· article· en· W3039023610 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

VenueBehavior Modification · 2020
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsWestern University
FundersBeijing Normal University
KeywordsThe ImaginaryGeneralizationPsychologyAutism spectrum disorderVideo modelingAutismModellingDevelopmental psychologyPsychotherapistTeaching methodMathematics education

Abstract

fetched live from OpenAlex

Many children with autism spectrum disorder (ASD) do not have symbolic play skills. One type of symbolic play involves playing with imaginary objects, in which a child displays play actions without actual objects. The purpose of this study was to evaluate the effects of video modeling on the acquisition, maintenance, and generalization of playing with imaginary objects in young children with ASD. Three male Chinese children (aged 4-5 years) with ASD participated in this study. A multiple-probe across three behaviors design was used. The results indicated that video modeling was effective in establishing and maintaining target symbolic play behaviors for the three children. Generalization to untaught imaginary play activities occurred in all three 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)0.000
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 score0.391
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.263
Teacher spread0.239 · 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