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Record W2982588724 · doi:10.5539/ijel.v9n6p287

English and French Borrowed Words for Euphemism in Jordanian Arabic

2019· article· en· W2982588724 on OpenAlexvenueno aff
Omar Mohammad-Ameen Ahmad Hazaymeh, Husein Almutlaq, Mufleh Amin AL Jarrah, Adam Al-Jawarneh

Bibliographic record

VenueInternational Journal of English Linguistics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSwearing, Euphemism, Multilingualism
Canadian institutionsnot available
Fundersnot available
KeywordsEuphemismArabicLinguisticsSociocultural evolutionReflexive pronounPerspective (graphical)PsychologySociologyComputer scienceArtificial intelligencePhilosophyAnthropology

Abstract

fetched live from OpenAlex

The present study aims to investigate the use of certain English and French words for euphemism in Jordanian Arabic from a sociocultural and linguistics perspective. The study based on an analytical analysis where data were collected from different places by the researcher himself, family members, informants besides taking benefit from related and similar studies. The study findings show that many English and very few French words are manipulated by Jordanian Arabic speakers to soften the effect of using Arabic direct words which have hard effect on people when they are used to describe someone or something. It also finds that the English and borrowed loanwords are used for various euphemistic domains. The study covers the English and French words that could be collected for the purpose of exploring the use them as euphemisms in Jordanian Arabic.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.321
Teacher spread0.306 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of English LinguisticsSame topicSwearing, Euphemism, MultilingualismFrench-language works237,207