Verb placement in L3 French and L3 German
Bibliographic record
Abstract
Abstract This article explores cross-linguistic influence and the relationship between surface structure and underlying syntactic structure in L3 acquisition of verb placement in L1 Norwegian L2 English learners of L3 German or French, respectively. In these languages, verb placement varies systematically. Previous research has found transfer from both L1 and L2 in similar language combinations. Using an acceptability judgment task, we tested verb placement in non-subject-initial and subject-initial sentences. Findings indicate that L3 French learners performed better on non-subject-initial sentences compared to subject-initial sentences, whereas the opposite was the case in L3 German. We argue that our findings can be explained by a generative account of verb movement and are compatible with an analysis where verbs do not move, or do not move far enough, in the L3 learners’ underlying syntactic representation. Following the assumption that verb movement is a costly operation, we argue that the syntactic operation verb movement is constrained by principles of economy in L3 acquisition, and that economy plays a role in determining cross-linguistic influence in multilingual acquisition. Our account is compatible with a uniform analysis of the acquisition of verb movement in L1, L2 and L3, and underlines the qualitative similarities in different acquisition processes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".