Long-distance wh-movement and long-distance wh-movement avoidance in L2 English: Evidence from French and Bulgarian speakers
Bibliographic record
Abstract
This article investigates spoken productions of complex questions with long-distance wh-movement in the L2 English of speakers whose first language is (Canadian) French or Bulgarian. Long-distance wh-movement is of interest as it can be argued that it poses difficulty in acquisition due to its syntactic complexity and related high processing load. Adopting the derivational complexity hypothesis, which has so far been applied to long-distance (LD) wh-movement in L1 acquisition and child second language acquisition, I argue that adult L2 learners also show evidence that questions with LD wh-movement are often replaced by alternative utterances with lower derivational complexity. I propose that such utterances, which are sometimes of equivalent length and with similar meaning to the targeted LD wh-structures, are avoidance strategies used by the learners as an intermediate acquisition resource. That is, such strategies are used as an escape-hatch from the derivational complexity of LD wh-movement. Overall, the results of this research indicate that the link between the number and complexity of derivational steps in a given structure is a fruitful area with strong potential in the second language acquisition field.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".