The nature and licensing of <i>hi:tʃ</i> elements in Persian
Bibliographic record
Abstract
Abstract This paper examines the nature of elements such as hi:tʃkӕs “anyone” in Persian, which have been described as either Negative Polarity Items ( Taleghani 2006 ) or Negative Concord Items ( Kwak 2010 ) in prior literature. Such claims have typically been used to motivate analyses of Persian NegP as being high in the clause structure, above TP. This is in contrast to more recent analyses which have argued for a low position of negation ( Kahnemuyipour 2017 ). Here, we present experimental evidence showing that c-commanding negation is not sufficient for licensing hi:tʃ elements, unlike English any . We also show that hi:tʃ elements have many properties in common with similar elements in Japanese and Korean, where there is less certainty in designating these as Negative Concord Items, and where negation is independently argued to be low. Thus, we claim, the distribution of hi:tʃ elements cannot be upheld as a proof of syntactically high negation, under either a polarity item or concord item analysis. We close the paper with a typological discussion, suggesting that the properties of hi:tʃ elements and their kin seem to be broadly shared among OV languages more generally.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".