«Orthodox Greek Emperors have had this many times»: Byzantine Example in legislative acts, panegyric and historical-political writings in Russia, late 17th – first quarter of the 18th century
Bibliographic record
Abstract
The article examines the use of examples from Byzantine history to explain current and historical events in Russia at the end of the 17th – first quarter of the 18th century. The usage of historical examples to clarify the meaning of events is one of the most common explanatory strategies. The source base of the research is mainly composed of panegyric literature, legislative acts, preparatory legislative documents, works of a historical and political nature. The history of Byzantium was well known in Russia at the end of the 17th century, however, the frequency of examples from Byzantine history was much inferior to the biblical and ancient ones. The article shows that in the studied chronological period, Byzantium was known to Russian authors mainly under the name «Greek Tsardom», the history of which dates back to the era of Emperor Constantine the Great. The name of this emperor and other emperors of early Byzantium (late Rome) were often mentioned as an example in the texts of the late 17th – first quarter of the 18th century. The article analyzes the use of facts from Byzantine history to clarify legislative acts, build historical analogies for Russian tsars, primarily Peter I. The role of the Byzantine example in the situation of Peter’s acceptance of the imperial title is also shown, as well as the application of the fall of Byzantium as political notation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".