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Record W2396305666 · doi:10.5539/elt.v9n7p87

A Syntactic Account for the Power of Verbs within X-Phemism: A Corpus-Base Exploration

2016· article· en· W2396305666 on OpenAlexvenueno aff
Maather Mohammad Al-Rawi, Nuha AlShurafa

Bibliographic record

VenueEnglish Language Teaching · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSwearing, Euphemism, Multilingualism
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsVerbPredicate (mathematical logic)ConceptualizationFocus (optics)GrammarPsychologyVerb phrasePhraseWord grammarNatural language processingSyntaxPhrase structure rulesComputer scienceArtificial intelligenceNoun phraseEmergent grammarNounPhilosophyRelational grammar

Abstract

fetched live from OpenAlex

The main aim of this paper is to examine the syntactic status of a selected text-corpus focus, with a special focus on the verb within its Verb-Phrase. The major claim is that the power of the verb in its VP is loaded syntactically through which the speaker’s desire of the doublespeak within X-Phemism is achieved. In order to fulfill this claim, a corpus-based exploration is applied on the selected data produced in Standard English. The analysis is accounted for a conceptualization of grammar that is based on general syntactic constraints on a well-formedness. The syntactic conceptualization (Chomsky, 2000; Ouhalla, 2002) is selected in its broad sense, as the basic framework where it best captures the syntactic role played by the verb-predicate and its various arguments.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0020.003
Scholarly communication0.0030.005
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.028
GPT teacher head0.325
Teacher spread0.297 · 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 designObservational
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

Citations0
Published2016
Admission routes1
Has abstractyes

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