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Record W2575948774 · doi:10.5539/ijel.v7n1p163

Pro-drop in Standard Arabic

2017· article· en· W2575948774 on OpenAlexvenueno aff
Bashir Mostufa Alnajadat

Bibliographic record

VenueInternational Journal of English Linguistics · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsnot available
Fundersnot available
KeywordsInflectionLinguisticsVerbSentenceSubject (documents)Computer sciencePhonological ruleMathematicsPhonologyPhilosophy

Abstract

fetched live from OpenAlex

The correlation between Pro-drop and the nature of verbal morphology is a universal principle. This syntactic phenomenon has been parameterized on the basis of verb inflection. Rich verbal inflection has been advocated to allow pro-drop subject. On contrast, if a language structure maintains a low level of verbal morphology, pro will not be dropped. This paper comes to show that Standard Arabic (SA) is a partial pro-drop language. It has null subject even with rich verbal inflection structure. Nonetheless, the paper shows that in some forms of imperative sentence that have poor verbal inflection, but the subject is optional. On the other side, and on the basis of minimalism, pro is asserted to have features that must be checked in the course of derivation. These features are case and agreement that can be valued at Specifier-head configuration to pro. The process of checking optimally tries to draw evidence for the minimum level of morpho-syntactic features that pro in SA carries.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.033
GPT teacher head0.292
Teacher spread0.259 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2017
Admission routes1
Has abstractyes

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Same venueInternational Journal of English LinguisticsSame topicSyntax, Semantics, Linguistic VariationFrench-language works237,207