Numeral <em>Any</em>: the view from Farsi
Bibliographic record
Abstract
This paper documents a dimension of crosslinguistic variation among Universal Free Choice Items. In English, the distribution and interpretation of any DPs containing a numeral ("numeral any") differs from that of any DPs with no numeral (Dayal 2005, 2013; Chierchia 2013). In contrast, the Farsi counterparts of any and numeral any mirror each other. Two competing analyses of the contrast between any and numeral any are assessed against the Farsi data – the Wide Scope Constraint Analysis (Chierchia 2013) and the Viability Constraint Analysis (Dayal 2013). The paper shows that, with minimal extensions, either analysis can capture the behavior of the Farsi counterpart of numeral any with distributive predicates. The situation changes, however, when the minimally modified analyses are assessed with respect to sentences with collective predicates: the extended Wide Scope Constraint Analysis captures the attested interpretation of those sentences, but the extended Viability Constraint Analysis rules them out, undergenerating.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".