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Record W4377197122 · doi:10.1002/bin.1953

Effects of video self‐modeling on the preference and reinforcer value of toys for children diagnosed with autism spectrum disorder

2023· article· en· W4377197122 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

VenueBehavioral Interventions · 2023
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsBrock University
Fundersnot available
KeywordsPsychologyReinforcementAutism spectrum disorderPreferenceAutismDevelopmental psychologyVideo modelingValue (mathematics)Cognitive psychologyAudiologySocial psychologyModellingStatistics

Abstract

fetched live from OpenAlex

Abstract A primary characteristic of autism spectrum disorder includes restrictive and repetitive patterns of behavior. Because having few preferred items and activities can lead to social, communicative, and educational barriers, it is important to increase the number of preferred stimuli in the individual's environment. One way to do this is through conditioned reinforcement via observation. Such procedures involve the acquisition of a skill or change in behavior as a result of indirect contact (i.e., observation) with contingencies received by others. While conditioning through observation has been shown to be effective, one novel approach is video self‐modeling. The purpose of the current study was to assess the effects of video self‐modeling on the preference and reinforcer value of toys for children diagnosed with autism spectrum disorder.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score0.451

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 head0.080
GPT teacher head0.350
Teacher spread0.270 · 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