Language and Learner Specific Influences on the Emergence of Consonantal Place and Manner Features
Bibliographic record
Abstract
This article focuses on the emergence of consonantal place and manner feature categories in the speech of first language learners. Starting with an overview of current representational approaches to phonology, we take the position that only models that allow for the emergence of phonological categories at all levels of phonological representation (from sub-segmental properties of speech sounds all the way to word forms represented within the child's lexicon) can account for the data. We begin with a cross-linguistic survey of the acquisition of rhotic consonants. We show that the types of substitutions affecting different rhotics cross-linguistically can be predicted from two main observations: the phonetic characteristics of these rhotics and the larger system of categories displayed by each language. We then turn to a peculiar pattern of labial substitution for coronal continuants in the speech of a German learner. Building on previous literature on the topic, we attribute the emergence of this pattern to distributional properties of the child's developing lexicon. Together, these observations suggest that our understanding of phonological emergence must involve a consideration of multiple, potentially interacting levels of phonetic and phonological representation.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".