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Record W3112358530 · doi:10.1163/15734218-12341458

Introduction

2020· article· en· W3112358530 on OpenAlexaff
Lisa Allette Brooks, Anthony Cerulli, Victoria Sheldon

Bibliographic record

VenueAsian Medicine · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicAnthropological Studies and Insights
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsScholarshipAsian studiesBiomedicineConversationTibetan medicineSouth asiaSocial scienceEnvironmental ethicsPolitical scienceSociologyHistoryTraditional medicineMedicineAnthropologyChinaLawPhilosophyBiology

Abstract

fetched live from OpenAlex

Abstract This opening piece introduces the eight articles in this special issue of Asian Medicine, all of which emerged out of the daylong Science, Technology, and Medicine in South Asia Symposium: Medicine and Memory, at the 2018 Annual Conference on South Asia in Madison, Wisconsin. These articles are concerned with the ways in which time and healing entangle across regions and healing traditions in South Asia, including Unani, Ayurveda, Naturopathy, and biomedicine. Linking the findings from these articles with recent scholarship, our conversation in the symposium moved beyond the notion of medical pluralisms to a notion of dynamic plurals, through historicizing regional and local diversities in practices and philosophies, often grouped under a single name by communities and practitioners. In an increasingly communalist and politically fractured modern South Asia, we suggest that the discussions in this special issue make a critical contribution to understanding how cultural institutions of knowledge function in society.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.542
Threshold uncertainty score0.774

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0070.004
Open science0.0020.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.4580.275

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 head0.039
GPT teacher head0.330
Teacher spread0.291 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2020
Admission routes1
Has abstractyes

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