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Record W4249050659 · doi:10.32920/14638383.v1

Islam's forgotten contributions to medical science

2021· preprint· en· W4249050659 on OpenAlexafffund
Ingrid Hehmeyer, Aliya Khan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicHistory of Medicine Studies
Canadian institutionsMcMaster UniversityToronto Metropolitan University
FundersUniversity of OxfordMcMaster University
KeywordsScholarshipIslamThe RenaissanceMedical sciencePeriod (music)ClassicsHistoryPolitical sciencePhilosophyArt historyMedicineLawAestheticsMedical educationArchaeology

Abstract

fetched live from OpenAlex

The transmission of medical knowledge can be traced to some of the earliest writings in human history. Yet a particularly fruitful period for advancement in medical science emerged with the rise of Islam. For the most part, Western scholarship belittles the contribution of the physicians of the Islamic world. They are usually perceived as simple purveyors of Greek science to the scholars of the Renaissance. However, the facts show otherwise.

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)0.007
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.031
Scholarly communication0.0050.003
Open science0.0000.004
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0040.001

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 head0.038
GPT teacher head0.320
Teacher spread0.282 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations4
Published2021
Admission routes2
Has abstractyes

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