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Record W4404839898 · doi:10.18732/hssa108

Reshaping Medical Learning

2024· article· en· W4404839898 on OpenAlexvenueno aff
Fabrizio Speziale

Bibliographic record

VenueHistory of Science in South Asia · 2024
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsComputer sciencePsychology

Abstract

fetched live from OpenAlex

The Ma‘dan al-šifā’-i Sikandar-šāhī is an extensive Persian handbook of Ayurvedic medicine made for Miyān Bhuwa ibn Ḫawāṣṣ Ḫān, a vizir of Sultan Sikandar Lodī (r. 1489-1517) to whom the book was dedicated. Miyān Bhuwa allocated considerable resources to achieving this translation project and hired scholars to translate the many parts of Ayurvedic books used to compile the Persian text. This article explores the reasons behind the production of the Ma‘dan al-šifā’ and proposes a new reading of certain features of this book. It enquires into its authorship and suggests that Miyān Bhuwa most likely only assembled the translations made from Ayurvedic texts. It discusses the epistemic and the practical issues raised in the preface, which criticizes the adequacy of Greco-Arabic thought in the Indian environment and the style of Ayurvedic texts, as well as the parallels with the Ṭoḍarānanda, a Sanskrit encyclopedia written in the late 16th century. The last part of the article looks at the conceptual structure of the Ma‘dan al-šifā’ and how the Sanskrit sources and their models shaped the organization of the sections of the Persian book. Moreover, it suggests that the overall framework of the book relied on the overlap of models of presentation of medical knowledge, a device meant to negotiate between the models of the Sanskrit sources and those of the Muslim readers.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.731
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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)0.0010.000

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 head0.038
GPT teacher head0.323
Teacher spread0.284 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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