Tap and trill clusters in typical and protracted phonological development: Challenging segments in complex phonological environments. Introduction to the special issue
Bibliographic record
Abstract
The papers in this crosslinguistic issue address children's acquisition of word-initial rhotic clusters in languages with taps/trills, that is, the acquisition of challenging segments in complex environments. Several papers also include comparisons with singleton rhotics and/or /l/ as a singleton or in clusters. The studies are part of a larger investigation that uses similar methodologies across languages in order to enhance crosslinguistic comparability (Bernhardt and Stemberger, 2012, 2015). Participants for the current studies were monolingual preschoolers with typical or protracted phonological development who speak one of the following languages: Germanic (Icelandic/Swedish); Romance (Portuguese/Spanish); Slavic (Bulgarian/Slovenian) and Finno-Ugric (Hungarian). This introductory paper describes characteristics of taps/trills and general methodology across the studies, concluding with predicted patterns of acquisition. The seven papers that follow are in a sense the 'results' for this introduction. A concluding paper discusses major findings and their implications for theory, research and clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".