Markedness and implicational relationships in phonological development: A cross-linguistic investigation
Bibliographic record
Abstract
PURPOSE: The complexity approach to speech disorders, based on the theoretical notion of phonological markedness, has been gaining interest over the last decade. In a nutshell, this approach suggests that the acquisition of phonologically marked units (e.g. complex onsets) implies the acquisition of less marked ones (e.g. singleton onsets). However, because the notion of markedness is, itself, subject to controversies, we need to constrain what types of implications can be generalised among language learners, within and across languages. METHOD: We report on longitudinal data from one phonologically-disordered and five typically-developing children documented across four different languages (English, French, German, Portuguese), using data from the PhonBank database (https://phonbank.talkbank.org). Using the Phon software program (https://www.phon.ca), we systematically analysed each longitudinal study for consonants in singleton onsets and codas as well as in onset clusters. RESULT: The implicational relationships supported by our study involve units of similar types (e.g. relations between different segmental categories), while relationships that involve different types of units or processes cannot be generalised across learners. CONCLUSION: A better understanding of implicational relationships makes the complexity approach more predictive of developmental patterns of phonology and related phonological disorders.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".