Compétences morphologiques compositionnelles en production chez des locuteurs francophones sains dans une tâche de dénomination
Bibliographic record
Abstract
Morphology is the study of the systematic correspondence between the form and meaning of words and their components. It can be divided into three areas: inflection, derivation and compounding. Compounding consists of word formation by the combination of lexical units, which have the same autonomy as words. In order to establish norms that can be used in the assessment of patients with language disorders, this study focuses on morphological compounding in healthy subjects. The objective of this study is to characterize the performance of healthy subjects in a picture naming task that includes simple words and compounds. All the words included in the task were nouns. Simple words were manipulated for length (between one and four syllables). Compounds were manipulated for internal structure (e.g., noun-noun: chou-fleur - cauliflower; adjective-noun: ouvre-boîte – can opener) and for transparency, that is the ease with which a compound can be interpreted based on its components. A transparency judgement was obtained prior to the main experiment with another group of participants. 37 participants (18 women) aged between 45 and 75 and divided into three education levels completed the naming task. Results do not show a difference between naming accuracy of simple words compared to compounds. However, performance was influenced by the structure and transparency of compounds. Overall, compounds formed with a preposition and transparent compounds were named more accurately than other stimuli. These two factors seem related, as the preposition provides compounds with a more transparent interpretation. These findings can guide the interpretation of performance following the assessment of patients with acquired language disorders.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".