A translation-matched, experimental comparison of three types of wh-island effects in Spanish and English
Bibliographic record
Abstract
According to the historical empirical consensus in the field, wh-argument extraction from embedded wh-questions gives rise to island effects in English, but not in Spanish. This observation – which was important for the development of a parameters-based theory of cross-linguistic variation in islands – has recently been challenged by experimental studies showing wh-island effects in both languages. However, these studies typically employ different materials and experimental conditions between languages, limiting direct comparison. Our study addresses this limitation by testing wh-islands in both English and Spanish with translation-matched materials. We present twelve acceptability judgment experiments with approximately 100 participants per experiment. In each language, we examine wh-argument extraction from three wh-clause types (introduced by whether, why and when) under two matrix verb types (know and ask), amounting to six wh-islands that are relevant to assess the reported contrasts. We test (i) for the presence or absence of wh-island effects in the two languages, (ii) for a gradient contrast in effect size, and (iii) for evidence of increased individual variation in Spanish as compared to English. We find (i) that wh-island effects are present in both English and Spanish, (ii) that they are rather large in both languages and larger in Spanish for most wh-island types, and (iii) that Spanish does not show more individual variation in wh-island effects than English. Our results speak against the cross-linguistic contrast as originally proposed, suggesting that its use as evidence for theories that encode cross-linguistic variation in wh-island effects might need to be reconsidered.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".