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Record W2781419951 · doi:10.5539/ijel.v8n2p288

Concord in English and Arabic: A Contrastive Study

2017· article· en· W2781419951 on OpenAlexvenueno aff
Zainab Kadim Igaab, Saja Mohammed Magrood Altai

Bibliographic record

VenueInternational Journal of English Linguistics · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSwearing, Euphemism, Multilingualism
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsPhenomenonSentenceArabicContrastive analysisGrammarComputer scienceNatural language processingPsychologyArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

The present study is a descriptive, analytic and contrastive one because it describes concord in English and Arabic to arrive at the similarities and differences between the two languages. This study aims at describing, analyzing and comparing concord in English and Arabic because the phenomenon of concord has attracted a great deal of attention in the recent years. It also aims at comparing and contrasting concord between the two languages by defining it, showing its syntactic and semantic aspects and illustrating its different types and rules. This study concludes that concord as a syntactic phenomenon exists in both languages. English deals with such a topic clearly and separately in grammar while in Arabic, it is not by being explained in sentences. Arab grammarians pay attention to the role of concord in the sentence and deal with it in different grammatical topics.

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)0.002
metaresearch head score (Gemma)0.010
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.003
Scholarly communication0.0020.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.381
Teacher spread0.348 · 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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