Cross-linguistic comparison of speech errors produced by English- and French-speaking preschool-age children with developmental phonological disorders
Bibliographic record
Abstract
Twenty-four French-speaking children with developmental phonological disorders (DPD) were matched on percentage of consonants correct (PCC)-conversation, age, and receptive vocabulary measures to English-speaking children with DPD in order to describe how speech errors are manifested differently in these two languages. The participants' productions of consonants on a single-word test of articulation were compared in terms of feature-match ratios for the production of target consonants, and type of errors produced. Results revealed that the French-speaking children had significantly lower match ratios for the major sound class features [+ consonantal] and [+ sonorant]. The French-speaking children also obtained significantly lower match ratios for [+ voice]. The most frequent type of errors produced by the French-speaking children was syllable structure errors, followed by segment errors, and a few distortion errors. On the other hand, the English-speaking children made more segment than syllable structure and distortion errors. The results of the study highlight the need to use test instruments with French-speaking children that reflect the phonological characteristics of French at multiple levels of the phonological hierarchy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".