Bibliographic record
Abstract
In this paper, we discuss the CP domain of embedded clauses in Spanish, specifically in the realm of que +embedded question constructions first discussed in Plann (1982). We argue for the existence of (at least) two distinct CP layers (following previous work by Lahiri 2002, Demonte & Fernández-Soriano 2009, and Suñer 1991, 1993). Following Suñer (1991, 1993), we argue that there are two semantically distinct classes of embedded clauses, although we depart from her by claiming that the relevant distinction should be formulated in terms of referentiality. We claim that her ‘true indirect questions’ are just one case of a non-referential embedded CP (another being a non-referential sentential complement to a non-factive verb). Moreover, we provide evidence that this difference in referentiality corresponds to a structural difference as well: embedded referential CPs have less structure than non-referential embedded CPs. We also offer a classification of embedded clauses based on the presence or absence of an extra CP layer ( c P) and the presence or absence of a question operator. Finally, we suggest that the overt spell-out of the non-referential head in Spanish embedded clauses is conditioned by the presence of a particular speech-act operator. Keywords: Spanish; indirect questions; factive and non-factive complements; referentiality; CP layers
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".