Acquisition of Turkish grammatical morphology by children with developmental disorders
Bibliographic record
Abstract
BACKGROUND: Many children with specific language impairment, Down syndrome or autism spectrum disorder have difficulty learning grammatical morphology, especially forms associated with the verb phrase. However, except for Hebrew, the evidence thus far has come from Indo-European languages. AIMS: This study investigates the acquisition of grammatical morphology by Turkish-speaking children with developmental disorders. Syntactic, perceptual and usage features of this non-Indo-European language were predicted to lead to patterns of atypical learning that would challenge and broaden current views. METHODS & PROCEDURES: Language samples were collected from 30 preschoolers learning Turkish: ten with developmental disorders, ten matched by age and ten by length of utterance. T-SALT then generated mean length of utterance, the total number of noun errors, the total number of verb errors and the per cent use in obligatory contexts for noun suffixes. Analyses also looked at the potential effects of input frequency on order of acquisition. OUTCOMES & RESULTS: Turkish children in the MLU-W control group, aged 3;4, used noun and verb suffixes with virtually no errors. Children in the group with atypical language showed more, and more persistent, morphological errors than either age or language peers, especially on noun suffixes. Children in the ALD and MLU-W groups were acquiring noun case suffixes in an order that is strongly related to input frequencies. CONCLUSIONS & IMPLICATIONS: These findings seem to reflect the influence of salience, regularity and frequency on language learning. Typical child-adult discourse patterns as well as the canonical SOV Turkish word order make verb suffixes perceptually salient, available in working memory and frequently repeated. The findings support the view that the language patterns seen in children with atypical development will differ from one language type to the next. They also suggest that regardless of language or syntactic class, children will have greater difficulty with those features of grammar that have higher cognitive processing costs.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".