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Record W6969161489 · doi:10.5281/zenodo.7785512

THE LIFE AND SCIENTIFIC HERITAGE OF ABUL BARAKAT NASAFI

2023· article· en· W6969161489 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic, Cultural, and Literary Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPeriod (music)InterpreterIslamCONQUESTNarrativeQuarter (Canadian coin)Interpretation (philosophy)

Abstract

fetched live from OpenAlex

Abul Barakat Nasafi, one of the great scholars who came from Nasaf, an outstanding Hanafi scholar, Quran interpreter and Maturid theologian, had deep knowledge in the field of tafsir, aqida and fiqh. For his contribution to Islamic sciences, he was awarded the honorary title of "Hafiz ad-Din" (Protector of Religion). In the books of biographers, it was mentioned that he was distinguished by seniority, progress, the ability to distinguish between strong and weak, and he was wary of transmitting rejected sayings and weak narratives in his books. He was known for righteousness, humility, asceticism, generosity, knowledge, open-mindedness, eloquence and fluency, love for the poor and students and favor to them. Although Abul Barakat Nasafi was born and raised in the city of Nasafi, he received his basic education in Bukhara and traveled to many countries. He earned respect for his knowledge in the countries he visited. After the conquest of Movarounnahr by the Mongols in the first quarter of the 13th century, a period of crisis began in the life of the peoples of this land with an ancient culture. It was during this period that Allama Abul Barakat Nasafi was born, who devoted his whole life to science and education and tried to revive the religious sciences.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.005
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.051
GPT teacher head0.284
Teacher spread0.233 · 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 designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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