Narrative discourse in French-speaking school-age children with and without specific language impairment : development, factors contributing to competency, and pragmatics
Bibliographic record
Abstract
This thesis is comprised of three manuscripts, each concerned with the narrative abilities of school-aged French-speaking children as assessed with the Edmonton Narrative Norms Instrument (ENNI). The three studies involved the same three groups of participants: one group of 12 children with SLI between the ages of eight year three months and nine year nine months, 12 typically-developing children of the same age and 12 typically-developing children matched on language abilities. Each child completed narrative tasks, language abilities tasks and cognitive tasks during one experimental session at their home. The first manuscript compares the ENNI narrative production (story grammar and referential expression) of school-aged French-speaking children to those of English-speaking children of the same age. It also provides data on the use of the ENNI with the school-aged French-speaking population with specific language impairment (SLI) by comparing their performance to the performance of their typically-developing (TD) peers. The second manuscript aims to identify which linguistic and cognitive skills contribute to narrative production of children with and without SLI at two levels: the micro- and the macro- levels. Finally, the third manuscript investigates the impact of variations in syntactic demand on the narratives produced by French-speaking children with SLI in comparison to the narratives produced by their age- and language- matched peers. The discussion summarizes the characteristics of French-speaking children's narrative production, the factors contributing to competency in narrative production, and explores the implications of these findings for language processing of children with and without SLI.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".