Processing of relative clauses with stylistic inversion in L2 French in adult learners with L1 English
Bibliographic record
Abstract
This thesis studies low to intermediate proficiency L2 speakers of French who have English as their L1 in order to better understand the role of proficiency and transfer in second language processing. Specifically, this study focuses on how these speakers process different relative clauses structures in their second language by having them read different sentences using a self paced reading task followed by comprehension questions. Participants read sentences that were of the following structures: subject relative clauses (SR), object relative clauses (OR), and object relative clauses with stylistic inversion (ORSI). The data provides evidence that proficiency as well as sentence structure have an effect on participants’ processing abilities. As participants’ proficiency increased so did their processing abilities which was evidenced by them recording faster reading times (RT) and achieving higher accuracy on comprehension questions. These increases in performance were only found to affect the OR and ORSI structures. Participants were consistent in their performance on SR, with results remaining stable as proficiency increased. Ultimately it appears that participants had the most difficulty with ORSI, the structure that is not allowed in their L1. OR had the next highest difficulty level, with participants having no difficulty with SR. The preference for SR over OR and ORSI suggests that the subject-object processing asymmetry was transferred from participants’ L1. This is further supported by participants’ poor performance on ORSI sentences. Based on comprehension results as well as RT data, participants appear to have begun acquiring the morphosyntactic process needed to process ORSI. These results plus the improved performance with increased proficiency, support the FTFA over the SSH.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".