Existential free choice items: The case of Farsi yek -i DPs
Bibliographic record
Abstract
Existential Free Choice Items (EFCIs) are interpreted as existential quantifiers in downward entailing contexts, but contribute stronger truth conditions when embedded under modals. When unembedded, their behavior differs: while Romanian vreun is ungrammatical (Fălăuş 2014), other EFCIs are grammatical and convey modality (Alonso-Ovalle & Menéndez-Benito 2015b). Farsi yek -i DPs instantiate a new profile: they pattern with other EFCIs in downward entailing and modal contexts, but differ in unembedded contexts, where they are grammatical, but do not convey modality. The paper derives this profile within an alternative- and exhaustification-based analysis of EFCIs (Chierchia 2013). Under this framework, EFCIs introduce two types of alternatives: scalar and (pre-exhaustified) domain alternatives. The behavior of yek -i DPs argues for the independence of the two types of alternatives and the splitting of scalar and domain exhaustification. EARLY ACCESS
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".