Filler–gap dependencies and islands in L2 English production: Comparing transfer from L1 Norwegian and L1 Swedish
Bibliographic record
Abstract
Embedded questions (EQs) are islands for filler–gap dependency formation in English, but not in Norwegian. Kush and Dahl (2022) found that first language (L1) Norwegian participants often accepted filler–gap dependencies into EQs in second language (L2) English, and proposed that this reflected persistent transfer from Norwegian of the functional structure that licenses such filler–gap dependencies. However, their results do not conclusively establish that the judgment patterns were specific to transfer from L1 Norwegian and not a general L2 effect. To address this issue, we conducted elicited production tasks comparing how L1 Norwegian and L1 Swedish speakers complete dependencies into declarative complement clauses and EQs both in their native languages and L2 English. Despite its similarity to Norwegian, Swedish prohibits the filler–gap dependency into EQs that Norwegian allows. We expected participants to complete dependencies that they considered grammatical with gaps and to avoid gaps where they considered them ungrammatical. Our results clearly indicate transfer: L1 Norwegian participants overwhelmingly used gaps when completing dependencies into EQs in both L1 and L2, whereas Swedish participants almost never used gaps in either language. We interpret our results as support for models that allow transfer of functional heads and their associated features from L1 to L2, and suggest that such transfer persists when the L2 input does not provide relevant evidence for restructuring.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".