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Record W3024736571 · doi:10.1080/15021149.2020.1758989

The evolution of high probability command sequences: Theoretical and procedural concerns

2020· article· en· W3024736571 on OpenAlexaff
Hunter C. King, Daniel Houlihan, Keith C. Radley, Duc Lai

Bibliographic record

VenueEuropean Journal of Behavior Analysis · 2020
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMomentum (technical analysis)Sequence (biology)PsychologyComputer scienceData scienceCognitive psychologyCognitive scienceMathematics educationEconomicsBiology

Abstract

fetched live from OpenAlex

Behavior analysts have studied John A. Nevin’s behavior momentum theory (BMT) since its introduction over three decades ago. The work of applied and translational researchers led to the development of the high-probability command sequence (HPCS). However, as BMT has been extrapolated to applied settings from experimental laboratories, a trend among applied behavior analysts has been to intermingle the terms, BMT and HPCS. Researchers must address this problematic trend because theoretical frameworks established in an experimental setting are conceptual while behavior modification technologies are procedural. This review aims to discuss several important distinctions between BMT and HPCS in an effort to encourage the exploration of behavioral momentum in areas other than noncompliance and education.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.117
GPT teacher head0.327
Teacher spread0.209 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2020
Admission routes1
Has abstractyes

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