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Record W3000379419 · doi:10.5539/ijel.v10n1p384

Insights into the Semantics of Reduplication in English and Arabic

2020· article· en· W3000379419 on OpenAlexvenueno aff
Khaled Mohammed Moqbel Al-Asbahi

Bibliographic record

VenueInternational Journal of English Linguistics · 2020
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
Fundersnot available
KeywordsReduplicationLinguisticsComputer scienceHistoryPhilosophy

Abstract

fetched live from OpenAlex

The paper aims to describe and compare the semantics of reduplication in English and Arabic. The paper shows more semantic similarities in reduplication than differences between both languages; although, Arabic reduplication is noted to be semantically more productive than English reduplication. Both languages divide reduplication into full/partial, free/bound, and continuous/discontinuous. Moreover, both languages share the senses of reduplication like; repetition, emphasis, intensity, onomatopoeia, contempt, affection, plurality, non-uniformity, and instability, nonsense, spread out, scatter, movement, contrast, continuity, completion, and lack of control. The semantic connection was developed between most of these concepts, which showed that ambiguity was common between both languages. Both the languages used reduplication in the nursery rhymes, lyrics, games, prayers, second language teaching, children’s phonics cartoons, advertisements, tongue twisters, slogans, newspaper headlines, and political and ideological rhetoric. These similarities support the belief of some linguists stating that different languages in the world share a variety of ‘universal’ semantic features. The study concluded that Arabic reduplication was semantically more productive than English reduplication.

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 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)0.000
metaresearch head score (Gemma)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.030
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 head0.018
GPT teacher head0.296
Teacher spread0.278 · 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 teacher head, not a consensus.

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

Citations7
Published2020
Admission routes1
Has abstractyes

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Same venueInternational Journal of English LinguisticsSame topicLanguage, Metaphor, and CognitionFrench-language works237,207