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Record W1967311410 · doi:10.1177/1088357614522288

The Combined Effects of Social Script Training and Peer Buddies on Generalized Peer Interaction of Children With ASD in Inclusive Classrooms

2014· article· en· W1967311410 on OpenAlexaff
Joel Hundert, Sarah Rowe, Erin Harrison

Bibliographic record

VenueFocus on Autism and Other Developmental Disabilities · 2014
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPsychologyAutism spectrum disorderAutismMultiple baseline designGeneralizationIntervention (counseling)Inclusion (mineral)Developmental psychologySocial psychology

Abstract

fetched live from OpenAlex

One of the challenges in supporting young children with Autism Spectrum Disorder (ASD) in inclusive classrooms is the generalization of improved social behaviors. Using a multiple-baseline design across participants, this study examined the generalized effects of social script training alone and combined with peer buddies on the interactive play of three children with ASD to play settings in inclusive classrooms where the training was not in effect. Social script training alone increased the interactive play of children with ASD when the intervention was in place, but did not generalize to another play setting when social script training was not being conducted. The addition of peer buddies combined with social script training produced a generalized increase in peer interaction to play settings in inclusive classrooms when theme-related play materials and adult assistance were unavailable. Implications of these results for inclusion of young children with ASD are discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.276
Teacher spread0.257 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations37
Published2014
Admission routes1
Has abstractyes

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