Habiletés morphosyntaxiques des enfants sourds porteurs d’implants cochléaires: une revue systématique = Morphosyntactic skills in deaf children with cochlear implants: A systematic review
Bibliographic record
Abstract
Thanks to an increasingly early implantation, the majority of children with cochlear implants (CCI) now succeed in reaching the standard with regards to overall language skills. However, some difficulties persist in morphology. This study therefore attempts to better understand morphosyntactic skills, the means to evaluate these skills and the tasks that provide a detailed description of same. In terms of methodology, a systematic review of the scientific literature published between 2000 and 2013 helped us identify 215 publications of which 18 studies analysed included children who received their implant before the age of 36 months. The results confirm the inferior performance in morphology by CCI’s, especially in the complex stages of grammatical development. Their typical errors, often omissions and substitutions, involve the less salient and more exacting morphemes with regards to perceptual, semantic, and grammatical processing, that is agreement markers for gender and number of determinants and clitic pronouns, as well as verbal flexions. Our study demonstrates that global tests, specialized tests, and questionnaires only provide a partial image of the difficulties in morphosyntax, such that the analysis of the spontaneous language still remains the best tool for the clinician to identify CCI’s real abilities and challenges. Future research should therefore attempt to develop standardised and more sensitive evaluation tools that are better adapted to the realities of CCI’s, leading to a more targeted and efficient intervention. © 2014 Canadian Association of Speech-Language Pathologists and Audiologists. All rights reserved.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".