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Record W4200188252 · doi:10.1002/jaba.896

Procedures for determining and then modifying the extinction component of multiple schedules for destructive behavior

2021· article· en· W4200188252 on OpenAlexaff
Scott A. Miller, Wayne W. Fisher, Brian Greer, Valdeep Saini, Madeleine Keevy

Bibliographic record

VenueJournal of Applied Behavior Analysis · 2021
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsUniversity of ManitobaBrock University
Fundersnot available
KeywordsReinforcementExtinction (optical mineralogy)SchedulePsychologySocial psychologyComputer science

Abstract

fetched live from OpenAlex

As a component of reinforcer schedule thinning following functional communication training, multiple schedules of reinforcement produce desirable rates and patterns of communication responses as an alternative response to destructive behavior. However, reinforcement schedule thinning is a gradual process that can take many sessions to obtain therapeutic goals. The desired outcome is that manding occurs only during signaled intervals of reinforcement with a sufficiently lean terminal schedule of reinforcement availability and low rates of destructive behavior. The purposes of this study were to (a) evaluate an assessment for informing the initial duration of extinction for alternative responding, (b) evaluate the utility of competing stimuli during extinction for alternative responding, and (c) assess a method for fading the availability of competing stimuli. With these procedures, all 4 participants experienced terminal schedules of reinforcement with rapid, robust reductions in destructive behavior soon after baseline. We discuss the implications and directions for future research.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0080.002

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 head0.158
GPT teacher head0.363
Teacher spread0.205 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations17
Published2021
Admission routes1
Has abstractyes

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