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Record W56688683 · doi:10.1177/215416470904400212

Predicting the Learning Ability of Children with Autism: The Assessment of Basic Learning Abilities Test versus Parents' Predictions

2009· article· en· W56688683 on OpenAlex
Lisa J. V. Schwartzman, Tricia Vause, Garry L. Martin, Christina Yu, Lindsay K. Campbell, Matthew C. Danbrook, Maurice A. Feldman

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

VenueEducation and training in developmental disabilities · 2009
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsSt.AmantBrock UniversityUniversity of Manitoba
Fundersnot available
KeywordsPsychologyTest (biology)Developmental psychologyAutismAutism spectrum disorderLearning disabilityTest validityPsychometricsClinical psychology

Abstract

fetched live from OpenAlex

The Assessment of Basic Learning Abilities (ABLA) test is a useful assessment and training tool for persons with developmental disabilities. The present study assessed the predictive validity of the ABLA test with 16 children diagnosed with an autistic spectrum disorder, eight who performed at ABLA Level 4 and eight who performed at ABLA Level 6. Twenty criterion tasks were selected, four at each of five ABLA levels. Predictions were made based on ABLA test performance and by parents as to whether each child would learn each of the criterion tasks (given certain conditions). The researchers then attempted to teach the 20 criterion tasks to each child until they reached either the pass standard or the fail standard of the ABLA test. Ninety-four percent of predictions based on ABLA performance were confirmed, and the ABLA test was significantly more accurate for predicting a child's performance than were parents.

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)0.002
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 score0.090
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.044
GPT teacher head0.324
Teacher spread0.279 · 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