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Record W7132200007

In terms of Asbab Al-Nuzul, Muqâtil b. Sulayman's tafsir and Ibn Hisham's as-Sīre : A comparative study

2024· dissertation· tr· W7132200007 on OpenAlexaboutno aff
Firdevs Yaşar

Bibliographic record

VenueMarmara University Open Access System · 2024
Typedissertation
Languagetr
FieldPsychology
TopicFamilies in Therapy and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsSymbol (formal)Period (music)Quarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

Mukâtil b. Süleyman’ın (ö. 150/ 767) et-Tefsîrü’l-kebîr’i, tefsir alanında sebeb-i nüzûl rivayetlerine sık sık yer vermesiyle bilinen, bir bütün olarak günümüze ulaşmış en eski tefsir eseridir. Bunun yanında Hz. Peygamberin hayatını ele alan temel kaynaklarından kabul edilen ve günümüze eksiksiz bir şekilde intikal eden en eski siyer kaynağı olarak İbn Hişâm’ın (ö. 218/ 833) es-Sîretü’nnebeviyye’si de yoğun bir şekilde esbâb-ı nüzul rivayetleri barındırmaktadır. Bu çalışmada Mukâtil ile İbn Hişâm’ın eserlerinde yer alan sebeb-i nüzûl rivayetleri mukayese edildi. Kendi alanlarında kaynak kabul edilen bu eserlerin mukayese edilmesi, erken dönemde tefsir ilmi ile siyer ilminin alakalarının ne düzeyde olduğunu anlamayı sağlamaktadır. Tezde Mukâtil’in tefsiri ile İbn Hişâm’ın es-Sîre’sinde yer alan sebeb-i nüzul rivayetleri ele alınıp, benzerlik ve farklılıkları incelendi. Bu inceleme, erken dönemde tefsir ve siyer ilimlerinde kaleme alınmış olan bu iki eserin sebeb-i nüzul rivayetleri üzerinden sahip oldukları tefsir bilgisi ile Kur’an âyetleri ve sebeb-i nüzul bilgisi olmadan siyerin tam anlaşılamayacağını, öte yandan siyer ve sebeb-i nüzul olmadan da Kur'ân âyetlerinin tefsirinin eksik kalacağını, tefsir ve siyer ilimlerinin erken dönemden itibaren sıkı bir bağa sahip olduğunu göstermekmektedir.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.002

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 head0.104
GPT teacher head0.457
Teacher spread0.352 · 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 designQualitative
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

Citations0
Published2024
Admission routes1
Has abstractyes

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