A comparison of two phonological screening tools for French-speaking children
Bibliographic record
Abstract
Purpose: To examine two screening tools for phonological production, the Evaluation Sommaire de la Phonologie chez l’enfant d’âge préscolaire (ESPP) and the Test de Phonologie du Français Canadien-Dépistage (TPFC-D), developed according to differing theoretical perspectives. The TPFC-D, designed according to nonlinear phonology, includes more words and contains a greater variety of segments across word structure as compared to the ESPP, which was guided by a linear phonological framework. The greater response rate to test items, time of administration, and phonological complexity were expected on the TPFC-D.Method: Each screening tool was administered to 14 4-year-old French-speaking children living in Central Canada. Paired samples t-tests compared children’s responses on the two tasks with regards to (a) response rate and time of administration, (b) an overall percentage of consonants correct (PCC) and percentage of vowel correct (PVC), and (c) complexity of productions (i.e. PCC and PVC in relation to word structure, Word Shape Match, Whole Word Match, Phonological Mean Length of Utterance (pMLU) and Proportion of Whole-Word Proximity).Result: Item response rates were higher for the TPFC-D whereas time of administration, PCC and PVC were similar for both the ESPP and TPFC-D. Complexity measures showed a higher proportion of deletions in clusters and higher pMLUs on the TPFC-D compared to the ESPP.Conclusion: Both screening measures are appropriate for speech-language pathologists who want to assess quickly pre-school-aged children. Since the TPFC-D is phonologically more complex, it is recommended for clinicians needing to screen children who likely present with multiple speech sound errors across their phonological system.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".