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Record W2785242018 · doi:10.5539/ies.v11n2p55

The Influence of Arabic Language Learning on Understanding of Islamic Legal Sciences—A Study in the Sultan Idris Education University

2018· article· en· W2785242018 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Education Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicArabic Language Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIslamArabicInterpretation (philosophy)ShariaIslamic cultureIslamic studiesSociologyLinguisticsPhilosophyTheology

Abstract

fetched live from OpenAlex

The most important in Islamic Legal Sciences is Arabic Language, because of its necessity for Muslims to understand the Islamic Legal Provisions in Qur’an and Hadith which are in Arabic and informed by Arabs, thus, to understand these sciences it is a must to learn Arabic to discern the meanings and benefits of Islamic Texts and Provisions. So based on induction and analysis, this study aims to illustrate the function of Arabic Language as a tool serves the Islamic Legal Sciences in the Sultan Idris Education University, by a questionnaire was disseminated among the students of Arabic Language to declare the efforts of Sultan Idris Education University in this field, especially within issues relate to understanding the Quran, its interpretation, and Islamic Legal Provisions.

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.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.069
GPT teacher head0.434
Teacher spread0.365 · 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