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Record W4391497044 · doi:10.30983/mj.v3i1.6304

Is Arabic Worth to Teach?

2023· article· en· W4391497044 on OpenAlexaboutno aff
Isral Naska

Bibliographic record

VenueModality Journal International Journal of Linguistics and Literature · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Linguistics, Cultural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsArabicPsychologyMathematics educationLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Abstract This research is a form of literature review that is part of my research that aims to investigate the views of Muslims in West Sumatra (Minangkabau Society) towards the Arabic language and how it drives their motivation to learn it. The lack of research on this issue in Indonesia, especially in the West Sumatran region, is the starting point of this study. In fact, a series of studies with the same theme have been conducted in various parts of the world such as in the United States (Belnap, 1987; Brosh, 2013; Husseinali, 2005, 2006; Nichols, 2014; Seymour-Jorn, 2004; Taha, 2007), Canada (Belnap, 1987), Malaysia (Abu et al., 2010; Aladin, 2010, 2013), Saudi Arabia (Al-Osaimi & Wedell, 2014), and the United Kingdom (Jaspal & Coyle, 2010). In terms of identity background, learners who establish some form of connection with the Arabic language are referred to as heritage learners (Brosh, 2013). I can conclude that religious identity is the strongest background that maintains Muslims' close relationship with Arabic. Abstrak Penelitian ini merupakan bentuk kajian pustaka yang merupakan bagian dari penelitian saya yang bertujuan untuk menyelidiki pandangan umat Islam di Sumatera Barat (Masyarakat Minangkabau) terhadap bahasa Arab dan bagaimana hal tersebut mendorong motivasi mereka untuk mempelajarinya. Minimnya penelitian tentang isu ini di Indonesia, khususnya di wilayah Sumatera Barat, menjadi titik tolak penelitian ini. Padahal, serangkaian penelitian dengan tema yang sama telah dilakukan di berbagai belahan dunia seperti di Amerika Serikat (Belnap, 1987; Brosh, 2013; Husseinali, 2005, 2006; Nichols, 2014; Seymour-Jorn, 2004; Taha, 2007), Kanada (Belnap, 1987), Malaysia (Abu dkk., 2010; Aladin, 2010, 2013), Arab Saudi (Al-Osaimi & Wedell, 2014), dan Inggris (Jaspal & Coyle, 2010). Dalam hal latar belakang identitas, pelajar yang menjalin hubungan dengan bahasa Arab disebut sebagai pelajar warisan (Brosh, 2013). Saya dapat menyimpulkan bahwa identitas agama merupakan latar belakang terkuat yang mempertahankan hubungan erat umat Islam dengan bahasa Arab.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.049
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.002
Scholarly communication0.0050.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0490.007

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 head0.296
Teacher spread0.269 · 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 designTheoretical or conceptual
Domainnot available
GenreCommentary

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
Published2023
Admission routes1
Has abstractyes

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