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History of Kyakhta Trade and Russian Merchants in Research Works of Zhargal Z. Tagarov (1952–2020): Commemorating the 70th Anniversary of His Birth

2022· article· en· W4388579897 on OpenAlexaboutno aff
Yuriy Kuzmin, Irina Kozyrskaya, Bato Tagarov

Bibliographic record

VenueJournal of Economic History and History of Economics · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEurasian Exchange Networks
Canadian institutionsnot available
FundersRussian Foundation for Basic Research
KeywordsEmpireChinaQuarter (Canadian coin)NegotiationAncient historyState (computer science)InterpreterHistoryEconomic historyPolitical scienceLawArchaeology

Abstract

fetched live from OpenAlex

The article is dedicated to the pioneering contribution of the Irkutsk scholar, associate professor of Baikal State University, Zhargal Z. Tagarov, to the study of the Kyakhta trade phenomenon and the Russia-Mongolia-China trade relations in the XVIII–XX centuries. The Irkutsk school of Oriental and Mongolian Studies has deep scientific traditions, laid down by the first Irkutsk translators and interpreters, who conducted complex negotiations with the officials of the Qing Empire and Outer Mongolia back in the XVII century. Irkutsk, due to the geographical and historical reasons, became the place of the making of the first Russian experts in Mongolian and Chinese studies, experts in the Chinese, Manchu and Mongolian languages. Providing Zhargal Z. Tagarov’s research works as an example, the authors make an attempt to show the contribution of the Irkutsk school of Mongolian Studies of the last quarter of the XX century to the research studies of complicated processes of economic and trade interaction between Russia, Outer Mongolia and the Qing Empire.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.005
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.115
GPT teacher head0.301
Teacher spread0.186 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2022
Admission routes1
Has abstractyes

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