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Record W2804489064 · doi:10.1002/jaba.468

Shaping complex functional communication responses

2018· article· en· W2804489064 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

VenueJournal of Applied Behavior Analysis · 2018
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsAcadia University
FundersFundação para a Ciência e a Tecnologia
KeywordsPsychologyProcess (computing)Functional analysisCognitive psychologySimple (philosophy)Computer science

Abstract

fetched live from OpenAlex

Response efficiency plays an important role in the initial success of functional communication training (FCT). Although low-effort functional communication responses (FCRs) have been shown to be most effective in replacing problem behavior; more developmentally advanced FCRs are favored later in the treatment process. Attempts to teach these more complex FCRs, however, often lead to the resurgence of problem behavior. In this study, we provide a detailed description of an effective shaping process applied within a changing criterion design to develop complex FCRs from simple FCRs without resurgence of problem behavior. Four children with various language and intellectual abilities participated in this study. A practical shaping procedure, suitable for typical teaching contexts, is described for two participants in Experiment 1. The necessity and efficacy of the shaping process are demonstrated with the participants in Experiment 2. Implications for practice and research 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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0120.000

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 head0.431
GPT teacher head0.416
Teacher spread0.015 · 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