Split noun phrase topicalization in Eshkevarat Gilaki
Bibliographic record
Abstract
Abstract Split noun phrase topicalization has been the subject of intense studies across languages in the syntactic literature of the last few decades. One of the key questions raised for these constructions is whether they involve syntactic movement or base-generation. This paper explores this phenomenon in two understudied Iranian languages, Gilaki (Northwestern Iranian, Caspien) and Persian. In particular, we explore splits in two contexts, possessive constructions and numeral constructions. We develop diagnostics for distinguishing the two derivational possibilities, movement or base-generation, for the cases under investigation. We show that while Gilaki uses both derivational possibilities, movement in possessor split and base-generation in numeral split, Persian only allows for the latter with very similar behavior. We argue that possessor split occurs when the whole possessum DP/DemP moves out of its base position in a small clause. Numeral split occurs when the NP is replaced by a null nominal element, which is associated with an overt or pragmatic antecedent. We end the paper with a discussion of why an operation, movement or base-generation, is available for one construction but not the other.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".