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 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 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".