Filler–gap dependencies and islands in L2 English production: Comparing transfer from L1 Norwegian and L1 Swedish
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it