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Record W1585664397 · doi:10.5539/jsd.v8n5p26

Velyaminov-Zernov V. V. and His Contribution to the Study of Historical Sources of Moslem Peoples

2015· article· en· W1585664397 on OpenAlexvenueno aff
Ramil M. Valeev, Firdaus G. Kalimullina, R. M. Valeev

Bibliographic record

VenueJournal of Sustainable Development · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEurasian Exchange Networks
Canadian institutionsnot available
FundersKazan Federal University
KeywordsHistoriographyHistoryNarrativeAncient historyMiddle EastOriental studiesGeographyArchaeologyArtLiterature

Abstract

fetched live from OpenAlex

The study of trends and outcomes in the development of modern Oriental historiography and source studies involves the study of scientific heritage and contributions of previous generations of scientists. Among those Russian and European orientalists of the XIX century, who made a great contribution to the development of the Russian Science of history and Oriental Studies, a special place is occupied by the Academician Vladimir Velyaminov-Zernov. Until today, the scientific heritage of V. V. Velyaminov-Zernov remained poorly studied. The main purpose of the article is to identify the contribution of V. V. Velyaminov-Zernov to the study of the history of the Turkic peoples and the medieval Muslim Khanates that emerged on the territory of modern Russia after the collapse of the Golden Horde. This article describes the results of research trips and archeographic work of V. V. Velyaminov-Zernov in different periods of his life. The main attention is paid to the Muslim narrative and epigraphic monuments, which were first introduced by him into scientific circulation. Analysis of his work allowed to define the views of Vladimir Velyaminov-Zernov on the continuity of ethno-cultural and religious traditions of the Muslims of the Middle Volga region, southern Urals, Urals, Central Asia and the Middle East.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.276
Teacher spread0.250 · 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
Published2015
Admission routes1
Has abstractyes

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