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Record W3184426950 · doi:10.1002/jaba.869

A comparison of two teaching procedures to establish generalized intraverbal‐tacting in children with autism

2021· article· en· W3184426950 on OpenAlexaff
Francesca degli Espinosa, Kate Wolff, Sophie Hewett

Bibliographic record

VenueJournal of Applied Behavior Analysis · 2021
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsAutismPsychologyDevelopmental psychologyStimulus controlNonverbal communicationStimulus (psychology)GeneralizationFrame (networking)Cognitive psychologyAudiologyComputer scienceMathematicsMedicinePsychiatry

Abstract

fetched live from OpenAlex

Previous research has investigated generalized intraverbal-tacting by teaching children with autism to respond using autoclitic frames. The present study compared the effectiveness and efficiency of a Frame and a No Frame procedure across counterbalanced stimulus sets with 4 children with autism. In the Frame condition, children were taught to respond using autoclitic frames (e.g., "Shape square," "Number two," "Color green," "It's mummy," "S/he is drinking") corresponding to the verbal antecedent ("What shape?", "What number?", "What color?", "Who is it?", "What is s/he doing?"). In the No Frame condition, intraverbal-tacting was established without the autoclitic frame. Irrespective of stimuli employed, 2 children acquired intraverbal-tacting only in the Frame condition. The other 2 children acquired intraverbal-tacting in both conditions, with the Frame procedure requiring fewer teaching trials for 1 child and producing greater generalization for the other. Implications for clinical practice and the role of additive intraverbal stimulus control of autoclitic frames 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)0.007
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.075
GPT teacher head0.390
Teacher spread0.315 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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