Evaluation of multisyllabic word production in Canadian English- or French-speaking children within a non-linear phonological framework
Bibliographic record
Abstract
Currently, there is no theoretically justified, evidence-based metric for evaluating segmental and prosodic components of multisyllabic words (MSWs). A pilot study evaluated a MSW metric embedded in non-linear phonological- and language-processing frameworks. Six MSWs were analyzed in 10 Canadian English-speaking 5-year-olds with typically developing speech, and eight French-speaking children, ages 3-4 years, with protracted phonological development (PPD). Mismatches were tallied (with and without vowels), with totals ranked by word and participant, then compared with ranks from Phonological Mean Length of Utterance (PMLU) and Percent Consonants Correct (PCC) tallies. For both groups, the number of different ranks was significant in comparisons of MSW metrics with PMLU and PCC. Rank orderings were systematically higher for English-speaking children using the MSW metric, with/without vowels, and for French-speaking children using the MSW metric with vowels. Overall, the MSW metric was particularly suitable for fine-grained differentiation of phonological accuracy in MSW production.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".