Remarks on Turkish interrogative complement clauses and verb subcategorization
Bibliographic record
Abstract
This chapter examines the interpretations of interrogative complement clauses in Turkish, with a focus on the embedded wh-words. Verbs such as unut- (forget) and hatırla- (remember) do not constitute an interrogative environment for the embedded wh-words, and the embedded wh-words are non-interrogative. Interestingly, the two verbs can yield either an interrogative or an indefinite reading. Meanwhile, san- (assume) and şüphelen- (suspect), behaving like desiderative and jussive verbs, do not license an embedded wh-word in their complement clauses. In contrast, düşün- (think), karar ver- (decide) and anla- (understand) yield ambiguous readings for embedded wh-words. Moreover, unlike the English counterparts, the embedded wh-words in complement clauses of sor- (ask) and merak et- (wonder) can obtain different scopes, but their interpretations remain interrogative in Turkish. The evidence suggests that the interpretations of wh-words depend on the embedding environments, and this suggests reevaluating the verb subcategorization frame based on the observation that wh-words are not consistently interrogative but also indefinite conditionally (Kratzer & Shimoyama, 2002). The problem relates to the syntax-semantics interface. In conclusion, there are three different types of verbs based on their attitudes towards the interpretations of the embedded wh-words. Keywords: Turkish, Verb subcategorization, Wh-words, Interrogative complement clauses, Syntax-semantics interface
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".