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«Evil for evil to die»: punishments of the ancient russian bishops (experience of a church judge of the XI - beginning of the XIV centuries)

2022· article· en· W4296593249 on OpenAlexaboutno aff
Павел Иванович Гайденко

Bibliographic record

VenueHistory Facts and Symbols · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicByzantine Studies and History
Canadian institutionsnot available
Fundersnot available
KeywordsBishopsPunishment (psychology)Quarter (Canadian coin)Byzantine architectureHistoryLawChristian ministryAncient historyClassicsPolitical sciencePsychologyArchaeologySocial psychology

Abstract

fetched live from OpenAlex

With all the attention of Russian historiography to the life of ancient Russian bishops, the problem of punishing bishops did not often attract the attention of researchers. If there was any interest in the noted topic, it was mainly within the framework of individual plots. However, a comprehensive consideration of the problem of punishment of ancient Russian bishops of the XI - the first quarter of the XIV was not carried out. Meanwhile, an analysis of the sources allows us to conclude that, for all the height of their ministry, the ancient Russian bishops not only regularly found themselves in the face of various courts, but were also subjected to various punishments, both ecclesiastical and those that took place in the practice of Byzantium and Russia. Within the framework of the presented article, an attempt was made to systematize the various punishments applied to bishops in the 11th - first quarter of the 14th centuries. The author comes to the conclusion that, despite their high status, the ancient Russian bishops were regularly subjected to various punishments. These punishments were not always fair. However, we can confidently say that the legal culture within the church was explained by the strong influence of Byzantine legal traditions. From a canonical point of view, punishments were far from always justified. But even in this case, the Old Russian scribes perceived them as corresponding to the realities of Old Russian reality and even fair.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.901
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.040
GPT teacher head0.221
Teacher spread0.181 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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