Individual profiles in protracted phonological development across languages: introduction to the special issue
Bibliographic record
Abstract
Although group studies provide necessary information about the range and frequency of phenomena in phonological development, individual profiles (case studies) can be used to describe entire phonological systems in detail. Profiles from different languages can highlight similarities and differences across languages that may be less obvious in group studies. The current issue presents profiles of children with protracted phonological development (PPD: speech sound disorders) from 16 languages (Akan, Kuwaiti Arabic, Bulgarian, Canadian English, Farsi, Canadian French, German, Greek, Icelandic, Japanese, Mandarin, Polish, European Portuguese, Slovenian, Granada Spanish, Swedish). Utilising a constraints-based nonlinear phonological framework, each profile describes a child’s strengths and needs in word structure, segments, features and their interactions and suggests an intervention plan. Where available, follow-up data from after clinical intervention are included. This introductory paper provides the theoretical background for the papers and reflects on the findings, drawing out particular themes and implications for phonological and developmental theories and clinical intervention. Read the transcript Watch the video on Vimeo ©2022 Taylor & Francis Group, LLC
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".