The island/non-island distinction in long-distance extraction: Evidence from L2 acceptability
Bibliographic record
Abstract
Experimental studies regularly find that extraction out of an embedded clause (“long-distance extraction”) results in a substantial degradation in acceptability but that the degradation is much greater when the embedded clause is an island structure. We explore these two facts by means of a series of acceptability experiments with L1 and L2 speakers of English. We find that the L2 speakers show greater degradation than L1 speakers for extraction out of non-islands, even though the two groups behave very similarly for extraction out of islands. Moreover, the L2 degradation with non-islands becomes smaller and more L1-like as exposure to the language increases. These initially surprising findings make sense if we assume that speakers must actively construct environments in which extraction out of embedded clauses is possible and that learning how to do this takes time. Evidence for this view comes from cross-linguistic variation in long-distance extraction, long-distance extraction in child English, and lexical restrictions on long-distance extraction. At a broader level, our results suggest that long-distance extraction does not come “for free” once speakers have acquired embedded clauses and extraction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".