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Record W2328506777 · doi:10.5539/ijel.v6n2p57

The Use of the /ki/ Variant among Young Karakis in Amman and Karak (Jordan) a Comparative Study

2016· article· en· W2328506777 on OpenAlexvenueno aff
Mahmoud El Salman

Bibliographic record

VenueInternational Journal of English Linguistics · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsExaggerationVariation (astronomy)Variable (mathematics)SocioeconomicsDemographyGeographyPsychologyComputer scienceLinguisticsSociologyMathematicsPhysicsAstrophysics

Abstract

fetched live from OpenAlex

This article is a sociolinguistic study conducted in Amman and Karak (Jordan) to investigate the linguistic variation in the speech of 48 Karaki informants who are originally from Karak and now living in Amman, and another 48 Karaki informants who still live in Karak. The [ki] variant of the (Vki) variable will be used as a basis to investigate this variation. The study shows that the [ki] is still preserved by the old in Karak and in Amman. It also shows that it is used by the young in a higher percentage than it is in Karak. The exaggeration of the use of it by the young, in contexts where the use of it is not preferable, for example in the presence of other people who do not use it, shows a strong desire to appear as local. It is also an attempt by the young to associate themselves with an area, namely the Karak area, which is known to be socially and politically important in Jordan. Appearing as local is not an aim in itself. It helps to achieve social aims. However, young females do not show the same desire. It disappears from their speech. Appearing as local is not the first priority by them. The rate of using it by them in Karak was in a low percentage. It becomes even lower in Amman. So, it is not uncommon to find women leads in linguistic change and to be innovative in this.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

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 studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.048
GPT teacher head0.335
Teacher spread0.287 · 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".

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Citations1
Published2016
Admission routes1
Has abstractyes

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Same venueInternational Journal of English LinguisticsSame topicLinguistic Variation and MorphologyFrench-language works237,207