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Record W3038010058 · doi:10.5539/jel.v9n4p94

Diagnosing and Teaching Students with Social Communication Disorder in Included Classrooms

2020· article· en· W3038010058 on OpenAlexvenueno aff
Christopher F. Mulrine, Betty Kollia

Bibliographic record

VenueJournal of Education and Learning · 2020
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsnot available
Fundersnot available
KeywordsAutismPsychologyAutism spectrum disorderAsperger syndromePervasive developmental disorderComprehensionClinical psychologyPsychiatryConversationRett syndromeDevelopmental psychology

Abstract

fetched live from OpenAlex

Autism Spectrum Disorder (ASD) was for many years considered to be one of five pervasive developmental disorders (PDD) as defined in the 4th edition of the Diagnostic Statistical Manual of Mental Disorders (DSM-IV-TR) published by the American Psychiatric Association (APA, 2000). These disorders included Autism, Rett Syndrome, Childhood Disintegrative Disorder, PDD-NOS (not otherwise specified), and Asperger’s syndrome. The 2013, fifth revision of the manual (DSM-5) presented a modification in the diagnosis for Autism Spectrum Disorder. It is now being diagnosed as an inclusive disorder of a range of symptoms or autism related symptoms from mild to severe (APA, 2013). It has dropped four of the previous diagnoses and is now only one encompassing disability called Autism Spectrum Disorder. Using the new DSM-5 diagnostic criteria some students who were previously diagnosed as having Asperger’s Syndrome do not fit the new Autism Spectrum Disorder criteria. These students might now be diagnosed with Social Communication Disorder (SCD). This diagnosis meets the symptoms presented by these individuals more appropriately. SCD describes the social difficulties and pragmatic language differences that impact comprehension, production, and awareness in conversation that are not caused by delayed cognition or other language delays.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.107
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.364
Teacher spread0.333 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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