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Thucydides, Procopius, and the Historians of the Later Roman Empire

2017· book· en· W2610940451 on OpenAlexaff
Conor Whately

Bibliographic record

VenueOxford University Press eBooks · 2017
Typebook
Languageen
FieldSocial Sciences
TopicClassical Antiquity Studies
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsSiegeAntiqueLate AntiquityHistoryRoman EmpireRhetoricLiteraturePoliticsClassicsAncient historyChoseArtPhilosophyPolitical scienceTheologyLaw

Abstract

fetched live from OpenAlex

This chapter discusses Thucydides’ influence on the historians of late antiquity, with a particular emphasis on Procopius. Topics include an overview of history writing in late antiquity; a look at some basic Thucydidean borrowings, from subject matter to types of episodes; a discussion of rhetoric and education in late antiquity and its role in fostering Thucydides’ impact; the particular place that Thucydides’ description of the siege of Plataea had in later accounts of sieges; and a discussion of Procopius’ particular engagement with Thucydides. The chapter argues that Thucydides’ evident influence was not the result of a malaise among those late antique historians who chose to write classicizing, “old-fashioned” histories, but rather of the immediate usefulness of Thucydides’ approach for those interested in war and politics.

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: Other · Consensus signal: Other
Teacher disagreement score0.005
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.012
Scholarly communication0.0040.003
Open science0.0000.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.027
GPT teacher head0.242
Teacher spread0.216 · 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
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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