La description d’image chez les adultes neurotypiques bilingues : analyse de la performance selon la langue utilisée.
Bibliographic record
Abstract
Background. Language assessment in adults often includes an analysis of oral discourse. Among the methods commonly used by speech-language pathologists, the picture description task is particularly prevalent in both formal and informal assessment contexts. This task allows for the collection of a language sample structured around a defined theme, thus facilitating comparisons between individuals. The obtained sample can be analyzed in terms of lexical and semantic content as well as syntactic structure. Performance on the picture description task may vary based on certain demographic characteristics; however, the performance of bilingual individuals based on the language used in this task remains underexplored. Objective. The main objective of this study was to compare the performance of neurotypical bilingual adults (French-English) in image description tasks in both French and English. Methods.Thirty neurotypical bilingual (French-English) participants were recruited in Ontario, Canada. Each participant described three images in both French and English. The recordings were transcribed and analyzed using the Computerized Language Analysis (CLAN) software, applying the MACS protocol for analyzing francophone discourse. The extracted linguistic variables were compared between the two languages. Results. Data analysis revealed significant differences between descriptions in French and English. The French descriptions contained more repetitions compared to English and little code-switching, suggesting linguistic insecurity where participants seemed to search for words, leading to repetitions in an attempt to deliberately avoid code-switching. An increased use of general verbs was observed in English, which could partly be explained by the difficulty of translating or retrieving more semantically complex specific verbs, and possibly due to lower proficiency in the other language. These differences were significant only for the “cat in the tree” image, highlighting that each task and stimulus imposes distinct linguistic and cognitive demands. Conclusion. This study highlights the differences in linguistic performance between French and English among bilinguals in a minority context. The findings underscore the importance for speech-language pathologists to consider these disparities when assessing linguistic competence in bilingual contexts. Finally, the study calls for the development of assessment protocols and tools tailored to the realities of bilingual populations to better address their specific needs and enhance speech-language pathology practices.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".