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 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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".