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Record W7116111184 · doi:10.33102/alazkiyaa110

دراسة في ضوء الأدبيات السابقة: مراجعة تحليلية لحروف الجر في القرآن الكريم

2024· article· W7116111184 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

VenueAl-Azkiyaa - Jurnal Antarabangsa Bahasa dan Pendidikan · 2024
Typearticle
Language
FieldSocial Sciences
TopicArabic Language Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRhetorical questionContext (archaeology)Expression (computer science)ConstitutionQuarter (Canadian coin)

Abstract

fetched live from OpenAlex

The Holy Quran is considered the primary and first reference for Muslims, as all the rulings and transactions mentioned in it are based on the constitution and rule that Muslims follow. The texts of the Holy Quran contain many grammatical and rhetorical structures. One of these structures is the use of prepositions to express rulings or stories mentioned in the Holy Quran. Therefore, this study seeks to review the literature that dealt with prepositions in the Holy Quran. It sought to know the contexts of previous studies in which quarter of the Holy Quran they were. And to identify the most common meanings of prepositions mentioned in these studies. This study used desk research by reviewing the librarian study of previous studies. The study found that many previous studies focused on studying prepositions from the first and third quarters of the Holy Quran. The results indicated that the high-frequency prepositions are "Lam," "Ba’," "Min," "Ala," and "Fi." These prepositions are essential for the expression of fundamental concepts such as ownership, means, origin, responsibility, and location. This study recommends conducting more studies in the context of the uses of prepositions in the Holy Quran.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.005
Science and technology studies0.0060.004
Scholarly communication0.0030.004
Open science0.0040.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0180.010

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 head0.024
GPT teacher head0.346
Teacher spread0.321 · 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