Nonverbal Learning Disabilities and Asperger Syndrome in Young Adults
Bibliographic record
Abstract
The purpose of this chapter is to explore data-driven hypotheses concerning linguistic similarities and differences in adults with nonverbal learning disabilities (NLD) and autism spectrum disorder (ASD). The focus of the chapter is on profiling linguistic, cognitive, and neuropsychological strengths and weaknesses seen in both clinical groups. A research sample of adults from 19 to 44 years of age is described. Findings include strengths in the breadth of vocabulary and weaknesses in semantic precision and integration. A secondary finding, in which responses to adult autism screening surveys distinguish both clinical groups from controls, and the clinical groups from one another, is presented. Patterns and trends in this data point to difficulties with verbal and nonverbal gestalt formation that are amenable to intervention. Clinical examples of interventions suggested by the data are provided, as they apply to post-secondary students of the same age as the sample.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".