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Record W2134190556 · doi:10.24297/jal.v5i3.2860

The Morpho-Syntax of Clausal Negation in Rural Jordanian Arabic

2015· article· en· W2134190556 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJOURNAL OF ADVANCES IN LINGUISTICS · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Linguistics, Cultural Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsLinguisticsNegationVerbSyntaxSentenceCovertPsychologyHead (geology)MathematicsPhilosophy

Abstract

fetched live from OpenAlex

In this paper, I argue that the Neg particles head their projections, and the negation in a hierarchical representation occurs between TP and VP. In future tense, I argue that the Aux can move to the Neg head just to pick the negation and then the negative particle and the Aux moves to T. I also show that speakers of RJA use different negation constructions depending on the structure and tense of the sentence. For example, the negative particle ma is a preverbal particle used with present and past verbs evenly. The negative particle ma¦-ƒ is a pre and post-verbal particle where ma is a proclitic and -ƒ is an enclitic. This particle is used with present verbs and past verbs. However, when used with present tense verbs, the proclitic ma becomes optional, whereas with past tense verbs the deletion of the proclitic ma results in an ungrammatical sentence. As for copular sentences, the particle miƒ is used to negate verbless copular sentences where there is a covert present tense verb. But, when the copular sentence is formed via a past tense verb, miƒ is no longer used. Instead, the negative construction maâ¦-ƒ is used.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.273
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it