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Record W4200204421 · doi:10.12745/et.24.2.4878

Sourcing Misfortunes: Translation and Tragedy

2021· article· en· W4200204421 on OpenAlexvenueno aff
F. Andrew Brown

Bibliographic record

VenueEarly Theatre · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicRenaissance and Early Modern Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTragedy (event)ImitationScholarshipPoliticsLiteratureHistorySociologyArtLawPolitical sciencePsychologySocial psychology

Abstract

fetched live from OpenAlex

From its first review to recent scholarship, critics have derided and dismissed the use made of translation in The Misfortunes of Arthur (1588). This essay reconsiders how the play approaches imitation by examining its translations from Senecan tragedy and Lucan’s De Bello Civili (ca 61-5 CE). With particular emphasis on Misfortunes’s ghost sequences and Oedipal echoes, this approach reveals the play’s engagement not just with the pedagogy and politics of Elizabethan England but also with innovations in dramatic form.

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)0.015
metaresearch head score (Gemma)0.041
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: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.041
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.005
Science and technology studies0.0040.038
Scholarly communication0.0130.013
Open science0.0010.005
Research integrity0.0030.005
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 head0.055
GPT teacher head0.223
Teacher spread0.168 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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