Diagnosing and Teaching Students with Social Communication Disorder in Included Classrooms
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".