Bibliographic record
Abstract
Abstract This paper explores the semantics of bare singulars in Turkish, which are unmarked for number in form, as in English, but can behave like both singular and plural terms, unlike in English. While they behave like singular terms as case-marked arguments, they are interpreted number neutrally in non-case-marked argument positions, the existential copular construction, and the predicate position. Previous accounts (Bliss, in Calgary Papers in Linguistics 25:1–65, 2004; Bale et al. in Semantics and Linguistic Theory (SALT) 20:1–15, 2010; Görgülü, in: Semantics of nouns and the specification of number in Turkish, Ph.d. thesis, Simon Fraser University, 2012) propose that Turkish bare singulars denote number neutral sets and that morphologically plural marked nouns denote sets of pluralities only. This approach leads to a symmetric correlation of morphological and semantic (un)markedness. However, in this paper, I defend a strict singular view for bare singulars and show that Turkish actually patterns with English where this correlation is exhibited asymmetrically. I claim that bare singulars in Turkish denote atomic properties and that bare plurals have a number neutral semantics as standardly assumed for English. I argue that the apparent number neutrality of bare singulars in the three cases arises via singular kind reference, which I show to extend to the phenomenon calledpseudo-incorporationand a construction that I callkind specification. I argue that pseudo-incorporation occurs in non-case-marked argument positions following Öztürk (Case, referentiality, and phrase structure, Amsterdam, Benjamins, Publishing Company, 2005) and the existential copular construction, whereas kind specification is realized in the predicate position. The different behaviors of bare singulars in Turkish and English stem from the fact that singular kind reference is used more extensively in Turkish than in English. Furthermore, while there are well-known asymmetries between singular and plural kind reference cross-linguistically, Turkish manifests a more restricted distribution for bare plurals than English in the positions where pseudo-incorporation and kind specification are in evidence. I explain this as a blocking effect, specific to Turkish, by singular kind terms on plural kind terms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".