Structural and semantic ambiguity of why-questions: An overlooked case of weak islands in English
Bibliographic record
Abstract
We argue that English why-questions are systematically ambiguous between a purpose and a reason interpretation, similarly to Mandarin, Russian, and Polish (contra Stepanov & Tsai 2008). We argue that the distinct semantic interpretations correspond to two distinct base-generated positions of why. While reason why is base-generated within CP (Rizzi 2001, Ko 2005), purpose why is adjoined to vP (Stepanov & Tsai 2008). Furthermore, we show that English purpose why, similarly to previously reported data from Mandarin, is only compatible with dynamic predicates with agentive subjects. We argue that this selectional restriction follows from two properties: (i) why semantically requires a proposition as its argument, and (ii) only dynamic predicates with agentive subjects have a syntactic structure that accommodates two adjunction sites of the relevant semantic type, i.e., they contain two distinct propositional levels (Bale 2007) and therefore two attachment sites for why. In contrast, propositionally simple predicates only have one propositional level and hence only one possible attachment site, which corresponds to the reason interpretation of why. Evidence for this proposal comes from the observation that only the lower why - associated with the purpose reading - is sensitive to negative islands, which suggests that its attachment site is below negation (vP), whereas the higher why is insensitive to island effects of this sort, which suggests that its base generated position is above negation (CP).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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 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".