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The Learning of Conceptual Categories for Students with Level 1 Autism Spectrum Disorder

2016· article· en· W2809984192 on OpenAlexvenueno aff
Manuel Ojea Rúa

Bibliographic record

VenueJournal of Intellectual Disability - Diagnosis and Treatment · 2016
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsnot available
Fundersnot available
KeywordsAutism spectrum disorderPsychologySpectrum (functional analysis)Cognitive psychologyAutismDevelopmental psychologyMathematics educationPhysics

Abstract

fetched live from OpenAlex

This article presents a study where the overall objective is to measure any changes found as a result of the application of a program to facilitate the development of the semantic memory of people with Level 1 Autism Spectrum Disorder, following the implementation of a specific program to facilitate the creation of conceptual categories. The study, based on a quasi-experimental design, analyzes the effectiveness of the program designed around four cognitive variables, comparatively observed in three groups of participants (N:19), distributed across one experimental group (N:7) and two control groups (made up of 7 and 5 participants respectively), conducted over three successive measures, 1 pre-test and 2 post-test measures at 6-month intervals. The results, found by repeatedly testing measures of intra-subject effects, show the effectiveness of the designed program, as it has aided in the development of meaningful learning for the members of the experimental group.

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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.078
GPT teacher head0.349
Teacher spread0.271 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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