The Syntax of Word Order Derivation and Agreement in Najrani Arabic: A Minimalist Analysis
Bibliographic record
Abstract
The paper aims to explore word order derivation and agreement in Najran Arabic (henceforth, NA) and examines the interaction between the NA data and Chomsky’s (2001, 2005) Agree theory which we adopt in this study. The objective is to investigate how word order occurs in NA and provide a satisfactorily unified account of the derivation of SVO and VSO orders and agreement in the language. Furthermore, the study shows how SVO and VSO word orders are derived morpho-syntactically in NA syntax and why and how the derivation of SVO word order comes after that of VSO order. We assume that the derivation of the unmarked SVO in NA takes place after applying a further step to the marked VSO. We propose that the default unmarked word order in NA is SVO, not VSO. Moreover, we propose that the DP which is base-generated in [Spec-vP] is a topic, not a subject. We adopt Rizzy’s split-CP hypothesis on the basis of which we assume the existence of a Top Phrase (TopP) projection in the clause structure of NA. We postulate that the phase head C passes its ϕ-features to the functional head T and the Edge feature to TopP. We assume that T in VSO lacks the Edge feature which motivates movement of the subject DP to [Spec-TP]. As a consequence, the subject of VSO structure remains in situ in the subject position of [Spec-vP]. In addition, it explores subject-verb agreement asymmetry (henceforth, SVAA) and shows that the asymmetry in NA is not related to word order differences but rather to gender agreement differences.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".