Singleton consonant onset acquisition in monolingual Granada Spanish-speaking preschoolers with typical versus protracted phonological development: Impacts of word structure and feature constraints
Bibliographic record
Abstract
While consonant acquisition clearly requires mastery of different articulatory configurations (segments), sub-segmental features and suprasegmental contexts influence both order of acquisition and mismatch (error) patterns (Bérubé, Bernhardt, Stemberger & Ciocca, 2020). Constraints-based nonlinear phonology provides a comprehensive framework for investigating the impact of sub- and suprasegmental impacts on acquisition (Bernhardt & Stemberger, 1998). The current study adopted such a framework in order to investigate these questions for Granada Spanish. Single-word samples of monolingual preschoolers in Granada (29 typically developing; 30 with protracted phonological development) were transcribed by native Spanish speakers in consultation with an international team. Beta regression analyses showed significant effects of age, developmental group, and word structure variables (word length, stress, position of consonants and syllables within the word); salience, markedness and/or frequency across the phonological hierarchy accounted for many patterns. The study further demonstrates the impacts of sub- and suprasegmental constraints of the phonological system on 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".