The acquisition of consonants in Québécois French: A cross-sectional study of pre-school aged children
Bibliographic record
Abstract
This study provides a systematic description of French consonant acquisition in a large cohort of pre-school aged children: 156 children aged 20-53 months participated in a picture-naming task. Five analyses were conducted to study consonant acquisition: (1) consonant inventory, (2) consonant accuracy, (3) consonant acquisition, (4) a comparison of consonant inventory to consonant acquisition, and (5) a comparison to English cross-sectional data. Results revealed that more consonants emerge at an earlier age in word initial position, followed by medial position, and then word final position. Consonant accuracy underwent the greatest changes before the age of 36 months, and achieved a relative plateau towards 42 months. The acquisition of consonants revealed that four early consonants were acquired before the age of 36 months (i.e., /t, m, n, z/); 12 intermediate consonants were acquired between 36 and 53 months (i.e., /p, b, d, k, g, ν, f, v, [symbol in text], l, w, ч/); and four consonants were acquired after 53 months (/s, з, ∫, j/). In comparison to English data, language specific patterns emerged that influence the order and pace of phonological acquisition. These findings highlight the important role of language specific developmental data in understanding the course of consonant acquisition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".