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Record W4297470743 · doi:10.1017/9781009063814.012

Narratives of Court Crises

2022· book-chapter· en· W4297470743 on OpenAlexaff
Angela Hug, Benjamin Kelly

Bibliographic record

VenueCambridge University Press eBooks · 2022
Typebook-chapter
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsYork University
Fundersnot available
KeywordsNarrativePolitical scienceHistoryLiteratureArt

Abstract

fetched live from OpenAlex

Most of the textual sources concerning the imperial court are relatively short; many recount anecdotes illuminating a single moment, a memorable saying, or a specific practice. The surviving works of history and biography do, however, contain a few longer narratives of connected sequences of events at court. Such narratives most commonly occur when historians and biographers describe crises, when events at court had wider implications for the political history of the Principate. Prompted by this observation, this chapter presents a selection of the richest crisis narratives. The narratives presented relate to: the fall of Claudius’ wife, Messalina; the loss of position at court suffered by Nero’s mother, Agrippina the Younger, and her ensuing murder by her son; and the assassinations of the emperors Domitian and Commodus.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0120.018
Scholarly communication0.0100.008
Open science0.0010.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.068
GPT teacher head0.304
Teacher spread0.236 · 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 designQualitative
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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