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Record W2159199502 · doi:10.1017/s0021911807000563

Critical Methods in Tibetan Medical Histories

2007· article· en· W2159199502 on OpenAlexaff
Frances Garrett

Bibliographic record

VenueThe Journal of Asian Studies · 2007
Typearticle
Languageen
FieldArts and Humanities
TopicIndian and Buddhist Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBuddhismContext (archaeology)NarrativePortraitMedical knowledgeSpace (punctuation)Extension (predicate logic)HistoryIdentity (music)EmpirePeriod (music)Tibetan medicineSociologyGenealogyLiteratureAestheticsMedicineAncient historyArtLinguisticsTraditional medicinePhilosophyArt historyMedical educationArchaeologyComputer science

Abstract

fetched live from OpenAlex

This paper addresses the development of scholastic medical traditions in Tibet through an extension of lists of physicians. I consider the debates that such lists and their accompanying narratives engender for Tibetan historians and reflect on the contributions they make to the identity of the medical tradition. By examining the structure and content of classificatory methods in medical histories, I argue that temporally organized lists document the place of medicine across time, geographically organized lists document the reach of medical knowledge across space, and thematically organized lists document the intertwining of medical knowledge and skill with other aspects of intellectual and civil life. In making these lists, medical historians paint a portrait of the Tibetan medical tradition that evokes connections to Buddhism and the strength and cosmopolitanism of the imperial period. Medical histories thus emphasize a picture of Tibet in the broader context of Asia- a Tibet whose empire lives on culturally or intellectually, if not militarily.

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)0.020
metaresearch head score (Gemma)0.024
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0110.008
Science and technology studies0.0130.057
Scholarly communication0.0100.009
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.087
GPT teacher head0.427
Teacher spread0.339 · 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 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

Citations19
Published2007
Admission routes1
Has abstractyes

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