Bibliographic record
Abstract
Phonological regressions or U-shaped development have frequently been observed in longitudinal studies of child speech production. However, the typology of which phonological patterns regress, and their implications for learning, have not been given much attention in the recent literature on constraint-based phonological development. One basic question is simply the definition of a phonological regression, as created by the grammar or other mechanisms, which is in turn dependent on the type of grammar and learner assumed. This paper systematically addresses the question of whether or not attested phonological regressions are incompatible with an error-driven approach to grammatical development, whereby each round of learning is predicted to move the learner closer to the target language. From this perspective, this survey discusses case studies of phonological regression in the literature, grouped according to their ease of explanation under error-driven learning. Three types are identified and exemplified: regressions which are easily explained with existing error-driven algorithms for constraint-reranking; regressions which can also be derived through error-driven learning by adopting an additional tool (for incorporating child-specific phonetic experience); and regressions whose error-driven motivation remains unclear. Another central theme of the survey is the degree of variation and lexical exceptionality among these regression patterns, and the extent to which such variability is captured in the learner's algorithms or grammar. Interim conclusions are provided, and necessary future directions for empirical and theoretical research are discussed. This article is categorized under: Linguistics > Language Acquisition Linguistics > Linguistic Theory.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".