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Record W2140536094 · doi:10.1177/215416470704200111

Predicting Optimal Preference Assessment Methods for Individuals with Developmental Disabilities

2007· article· en· W2140536094 on OpenAlexaff
Kendra Thomson, Diana Czarnecki, Toby L. Martin, C. T. Yu, Garry L. Martin

Bibliographic record

VenueEducation and training in developmental disabilities · 2007
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPsychologyPreferenceMultiple disabilitiesDevelopmental psychologyClinical psychologyApplied psychologyStatisticsMathematics

Abstract

fetched live from OpenAlex

The single-stimulus (SS) preference assessment procedure has been described as more appropriate than the paired stimulus (PS) procedure for "lower functioning" individuals, but this guideline's vagueness limits its usefulness. We administered the SS and PS preference assessment procedures with food items to seven individuals with severe or profound developmental disabilities who scored at level 2 of the Assessment of Basic Learning Abilities (ABLA) and seven who scored at ABLA level 3. Thirteen of the 14 participants also received these assessments (PS and SS), with non-food items. The two procedures were about equally effective for both groups, and with both types of stimuli, although the PS procedure produced more refined preference hierarchies. Most participants showed moderate to high correlations in preference scores between the two procedures for both food and non-food items. These results suggest that, for individuals who score at either ABLA level 2 or ABLA level 3, the SS and the PS procedures are equally likely to identify preferred stimuli.

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)0.005
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.309
GPT teacher head0.452
Teacher spread0.143 · 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 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

Citations10
Published2007
Admission routes1
Has abstractyes

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