Exploring the Impact of Regularity, Frequency and Phonological Complexity on Morphological Production in Children with DLD and Phonological Disorders
Bibliographic record
Abstract
This study examined the effects of regularity, frequency, and phonological complexity on morphological production in Turkish-speaking children with developmental language disorder (DLD) and phonological disorder (PD) compared to typically developing (TD) peers. Thirty children (ages 4–6) completed elicited production tasks using real and nonce words with regular and irregular noun and verb suffixes. DLD children showed lower accuracy in tasks involving consonant voicing and epenthesis and performed significantly worse on irregular suffixation, often substituting irregular forms with familiar ones. Nonce word production confirmed these challenges. Random Forest analyses indicated that phonotactic probability best predicted TD performance, while lemma frequency and phonological neighborhood density were more influential for DLD and PD groups, respectively. These findings suggest that DLD children rely on familiar, regular forms to manage morphological complexity, reflecting distinct processing strategies compared to PD and TD peers.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".