Spanish embedded question island effects revisited: an experimental study
Bibliographic record
Abstract
Abstract It is often reported that embedded questions (EQs) are not syntactic islands in Spanish. However, some authors have observed that the acceptability of filler-gap dependencies (FGDs) into Spanish EQs varies with the EQ-embedding verb: FGDs into EQs under responsive verbs (e.g., know ) do not result in island effects, but FGDs into EQs under rogative verbs (e.g., ask ) do yield island effects. One account attributes the contrast to a structural difference between the two EQs, due to which ask -EQs violate Bounding constraints, but know -EQs do not. In two acceptability studies we investigated the reliability of verb-dependent island effects in EQs introduced by si ‘whether’ and cuándo ‘when’. We found no qualitative acceptability differences between ask and know EQ-island sentences, suggesting that the syntactic islandhood of Spanish EQs is not verb-dependent. Nevertheless, average island effects were numerically greater with ask , suggesting the presence of a non-syntactic constraint. In addition, FGDs into whether -EQs were generally acceptable, whereas FGDs into when -EQs obtained unacceptable average ratings and highly variable judgments. We argue that in neither case there is a Bounding constraint violation. Instead we explore alternative potential explanations for the differences in terms of features, presuppositions and processing pressures.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".