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“Elephant Ayurveda” at the 18th-century Russian Court: Medical Pluralism, Technology Transfer, and Avatars of Power

2024· article· en· W4403320576 on OpenAlexfundno aff
Maria Pirogovskaya

Bibliographic record

VenueAntropologicheskij forum · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Science and Medicine
Canadian institutionsnot available
FundersUniversity of CalcuttaMcGill UniversityHarvard UniversityUniversity of ChicagoUniversity of OxfordQueen's UniversityJohns Hopkins University
KeywordsPluralism (philosophy)Technology transferPower (physics)Political scienceLawPhilosophyBusinessEpistemologyInternational trade

Abstract

fetched live from OpenAlex

The article analyzes Russian documents of the 18th century related to the delivery of elephants to the Russian court, their allowance and treatment. The study focuses on the political and technological aspects of the animals’ life. The relocation of elephants from eastern powers to Moscow and St Petersburg was part of animal diplomacy and was conditioned by the status of animals as avatars of power. In turn, elephantine political charisma encouraged both parties — the donor and recipient of living gifts — to invest considerable effort in maintaining their well-being. For this purpose, the Russian Empire imported elephant keepers (mahouts), oriental food and Ayurvedic medicines. The author proposes to consider people, equipment, food, and medicinal matter as components of a technological system. The relocation of elephants between different states is interpreted as a case of technology transfer, which follows the same logic as the more explored scalable technologies. In this way, the therapeutic landscape of the Russian Empire was enriched with enclaves of Eastern medical knowledge that were conditioned by animal life. The results of the study not only complicate current ideas about historical configurations of medical pluralism, but also provide new insights into the grand narrative about the trajectories of knowledge importation in early modern Russia.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.919
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.014
GPT teacher head0.244
Teacher spread0.230 · 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.

Study designNot applicable
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

Citations1
Published2024
Admission routes1
Has abstractyes

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