L2 French Learners’ Processing of Object Clitics: Data from the Classroom
Bibliographic record
Abstract
The purpose of this study was to assess whether the well-documented paucity of object clitics in L2 French production reflects difficulties learners have comprehending these forms in classroom input. To this end, an aural French-English translation task was used to determine the extent to which university-level L2 learners of French (N=152) were able to process and encode the meaning of the object clitics me, te, la, l’, les, lui, leur, y and en. An analysis of the translations revealed variation in performance across clitic types (19-75% accuracy) and as a function of learners’ proficiency level and educational background. There was a positive relationship between L2 proficiency and clitic processing. Post-French immersion learners were better able to process and encode clitics than their post-core French peers. As a group, the learners were only 54% accurate, with their mistranslations of object clitics indicating incomplete use of gender, number, animacy and case markings to link these forms to their co-referents. An under-reliance on animacy and agreement cues by these L2 learners suggests the need for explicit instruction on the importance of syntactic and discourse-pragmatic information in clitic comprehension.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".