Sensitivity to Parasitic Gaps Inside Subject Islands in Native Speakers of English and Najdi Arabic Learners of English
Bibliographic record
Abstract
This study examined second language (L2) learners’ sensitivity to parasitic gaps (PGs) in English. In PG constructions, an unacceptable gap inside an island becomes acceptable when it is combined with an acceptable gap (e.g., whati did [ISLAND the attempt to repair __pg] ultimately damage__i?). Linguists have often viewed PGs as marginally acceptable by native speakers, but recent studies have shown that they are fully acceptable. This phenomenon, however, has received little attention in L2 research. Thus, this study’s purpose was to test sensitivity to PGs in L2 learners. In an acceptability judgment task, native speakers of English (n = 32) and Najdi Arabic learners of English (n = 38) used a 10-point scale to rate their acceptability of wh-questions with PGs, wh-questions with gaps inside subject islands, and wh-questions with gaps inside non-island structures. Like native speakers of English, Najdi Arabic learners of English rated wh-questions with PGs not only more acceptable than they rated ungrammatical wh-questions with gaps inside subject islands but also as highly as they rated grammatical wh-questions with gaps inside non-island structures. These results suggest that PGs are fully acceptable by Najdi Arabic learners. The Najdi Arabic learners’ sensitivity to PGs in English supports the Full Transfer/Full Access Hypothesis (Schwartz & Sprouse, 1996), which claims that advanced adult L2 learners can acquire L2 properties regardless of L1.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".