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Record W1580185868 · doi:10.5539/jsd.v8n7p153

Generations in Historical Science: The Hvostovs Dynasty

2015· article· en· W1580185868 on OpenAlexvenueno aff
Oleg V. Sinitzyn, Albina Imamutdinova

Bibliographic record

VenueJournal of Sustainable Development · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHistory and Developments in Astronomy
Canadian institutionsnot available
FundersKazan Federal University
KeywordsHistoryClassicsDaughterSociology of scientific knowledgeSociologyGenealogySocial scienceLawPolitical science

Abstract

fetched live from OpenAlex

The thematic justification of the researched problem comes from the need to identify a family basis, linking generations from parents to children transmitting not only traditions and eternal values, but also scientific knowledge, particularly when this knowledge is not simply accepted and perceived, but also implemented professionally, including as a continuation of the life of older generations. The purpose of the article is to examine and catalogue the materials about the Hvostovs Russian dynasty of historians. They are: a leading historian of antiquity at the beginning of the 20th century Mikhail Mikhailovich Hvostov (1872-1920), his son, a prominent Soviet historian of international relations and foreign policy of Russia, academician Vladimir Mikhailovich Hvostov (1905-1972) and his daughter, the doctor of historical sciences, a leading scientific employee of the Institute of world history, Russian Academy of Sciences, the head of the

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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.247
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 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

Citations2
Published2015
Admission routes1
Has abstractyes

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