Language and Communicative Functions as well as Verbal Fluency in Children with High-Functioning Autism
Bibliographic record
Abstract
The study was designed to investigate selected aspects of language and communicative functions as well as verbal fluency in children with HFA. The study group comprised 51 children, aged 10-12, including 23 subjects diagnosed with High-Functioning Autism, with normal IQ and able to communicate verbally, as well as a group of 28 controls. The applied tools included RHLB-PL Battery, a verbal fluency task and WISC-R Vocabulary subtest. The findings show significantly varied profiles of the investigated functions in the group of children with HFA. In comparison with their peers, they have greater difficulties drawing logical conclusions from stories. They find it difficult to grasp humour conveyed by linguistic expression and by metaphors, presented with the use of both linguistic materials and drawings. They have lower capacitates for understanding prosodic (emotional and language) aspects of utterances addressed to them. It has been established that they are able to correctly understand isolated words and recognize their designates despite the present distractors. No generalized deficits have been found in the subjects’ verbal fluency. In comparison to the controls, the children with HFA generated similar number of words matching the phonemic criterion. Furthermore, their performance showed no perseverations, and comparably frequent clustering and switching. Lexicon matching the semantic criterion was more difficult to access for the children with HFA than for the controls. Children with HFA had difficulties in defining familiar words.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".