NegP Located Above TP: Evidence from Standard Arabic (SA) and Saudi Northern Region Dialect of Arabic (SNRDA)
Bibliographic record
Abstract
The paper examines the properties of sentential negation in Standard Arabic (henceforth SA) and Saudi Northern Region Dialect of Arabic (henceforth SNRDA), focusing on similarities and differences in use and distribution (Note 1). In this paper, I propose that that the sentential negation facts of standard and dialectal versions of Arabic receive a unified account despite their apparent differences. I provide some empirical and conceptual evidence of the workability for the Neg-Above-T analysis over the Neg-Below-T analysis. NegP cannot remain lower than TP in Standard Arabic as the language employs V-to-T raising to drive the VSO from SVO word order. NegP in SNRDA should be higher than TP as it precedes non-verbal predicates (nominals, adjectivals, prepositionals, and adverbials) and some TP/CP located elements (expletive/ (indefinite) pronominal subjects and the future tense expressing element raaħ, and adverbials hosting pronoun subject clitics like ʕumri/uh.
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".