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Record W4399604202 · doi:10.1093/ahr/rhae136

Tatiana Tairova-Yakovleva. <i>Ivan Mazepa and the Russian Empire</i>.

2024· article· en· W4399604202 on OpenAlexaboutno aff
Brian Davies

Bibliographic record

VenueThe American Historical Review · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEmpireHistoryAncient historyArt

Abstract

fetched live from OpenAlex

Tatiana Tairova-Iakovleva has published several important monographs on the political and social development of seventeenth and eighteenth-century Ukraine, including studies of the archive of the Hetmanate’s Baturyn capital and the archive of the Russian Empire’s Little Russian Chancellery (Malorossiiskii prikaz). Ivan Mazepa and the Russian Empire is an ably executed English translation by Jan Surer of her 2004 study, Ivan Mazepa i Rossiiskaia imperiia: istoriia predatel’stva, which has been revised to include new material. The subject—the rise and fall of Hetman Ivan Stepanovych Mazepa (1639–1709)—is an important and still controversial one. Mazepa’s abandonment of his strategic partnership with Russian Tsar Peter the Great and his short-lived shift of allegiance to Swedish King Charles XII in 1708, the height of the Great Northern War, has been condemned as treasonous by most Russian historians and got Mazepa anathematized by the Russian Orthodox Church. But in recent years, Ukrainian political and cultural leaders have taken up Mazepa as a martyr and hero of the Ukrainian independence struggle. Tairova-Yakovleva examines Mazepa’s career and its larger political and cultural context in remarkable detail, making extensive use of archival sources, and her treatment of Mazepa is nuanced, recognizing the conflicts between his political aspirations and the heavy restrictions on actual political possibilities during the Northern War.

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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.023
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0230.010

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 head0.024
GPT teacher head0.251
Teacher spread0.227 · 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
GenreReview

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
Published2024
Admission routes1
Has abstractyes

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