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Record W2022354464 · doi:10.1163/15700674-12342139

Early Muslim Medicine and the Indian Context: A Reinterpretation

2013· article· en· W2022354464 on OpenAlexaff
Miri Shefer-Mossensohn, K. Abou Hershkovitz

Bibliographic record

VenueMedieval Encounters · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Studies and History
Canadian institutionsMcGill University
Fundersnot available
KeywordsReinterpretationNinthContext (archaeology)Muslim worldArabicTheme (computing)South asiaMedieval historyIslamHistoryClassicsSociologyAncient historyArtPhilosophyAesthetics

Abstract

fetched live from OpenAlex

Abstract The past few decades have witnessed a remarkable shift in the way scholars study the field of sciences in Muslim societies. Up to the 1980s, research focused on Muslim scientists’ role as transmitters of science to the West, and as contributors to Western science. The Muslim world was commonly viewed as a link between ancient Greece and Latin Christendom, its scholars serving as translators of Greek treatises, and as preservers of Greek knowledge. Recently, the theme of Indian-Muslim cultural-scientific relations has attracted growing attention. Following this trend, we maintain that the eighth and ninth centuries reveal an interaction between Indian and Muslim medicine and physicians. Building on the past work of scholars such as Michael W. Dols and more recently Kevin van Bladel, we reinterpret medieval Arabic sources to reveal that the interest in Asian science was not a brief and untypical phenomenon that lacked long-lasting implications. By rereading Arabic chronicles and biographical dictionaries, we will portray how a rather brief contact between ʿAbbāsid Iraq and India proved to yield enduring influences. We will focus on two aspects of Muslim medical practice for demonstrating the Indian connection: the presence of Indian physicians in Baghdād in and around the ʿAbbāsid court, and the emergence of early Muslim hospitals.

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 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)0.001
metaresearch head score (Gemma)0.000
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 score0.155
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.011
GPT teacher head0.258
Teacher spread0.246 · 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 teacher head, 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

Citations9
Published2013
Admission routes1
Has abstractyes

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