Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".