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Record W2981152352 · doi:10.5325/chaucerrev.54.4.0373

Feriby's “Lament for Richard II” and the English Literary History ca. 1400

2019· article· en· W2981152352 on OpenAlexaff
David R. Carlson

Bibliographic record

VenueThe Chaucer Review · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicMedieval Literature and History
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsLamentHistoriographyLiteratureHistory of literatureStyle (visual arts)HistoryParliamentWriting styleClassicsPhilosophyArtPoliticsArchaeologyLaw

Abstract

fetched live from OpenAlex

Abstract William Feriby was an historic actor: Richard II's secretary between the “Revenge Parliament” of 1397–98 and the deposition of the king in September 1399; subsequently, a contributor to the deposition proceedings; and, finally, a rebel against Henry IV in January 1400. Feriby was also a literary figure, the author of an historical “Lament for Richard II,” amongst other things, written in a highly mannered style of a sort to indicate an interest on Feriby's part in the purely literary: imagination and form. Though Feriby's prose style is distinctive, he was not the only historic-historiographical figure who manifested an interest in the strictly literary at the same moment, ca. 1400. The writings of Adam Usk, Thomas Favent, the Historia vitae author, and Thomas Brinton—even the deposition proceedings and the documentary record of them (the “Record and Process”)—are comparably (though differently) literary. The relations between Ricardian history and literature can be reconceived, dialectically, by light of such evidence: the literature is historical and historic, but, also, the history itself and historiography are literary.

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)0.003
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.052
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.004
Science and technology studies0.0020.004
Scholarly communication0.0050.002
Open science0.0000.001
Research integrity0.0010.002
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 head0.026
GPT teacher head0.215
Teacher spread0.189 · 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
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
Published2019
Admission routes1
Has abstractyes

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