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Record W2898977283 · doi:10.3366/tal.2018.0352

Thomas Twyne's Appropriation of Thomas Phaer's <i>Æneidos</i>: ‘Worke unperfyt’ Perfected?

2018· article· en· W2898977283 on OpenAlexaff
Susanna Braund

Bibliographic record

VenueTranslation and Literature · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies of British Isles
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAppropriationEPICCompetition (biology)Element (criminal law)ClassicsClosure (psychology)LiteratureKey (lock)PhilosophyHistoryHumanitiesArtLinguisticsLawPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Abstract: This paper attempts to reconstruct from the limited evidence available the rationale and development of Thomas Twyne's completion (1573–84) of Thomas Phaer's unfinished English translation of the Aeneid (1555–60). In Phaer's hands, it is suggested, ‘continuation’ and ‘completion’ gradually turn over time into ‘competition’, ‘absorption’, and even ‘appropriation’. A key element in this process is Phaer's decision to add to the twelve books of Virgil's epic his translation of Maffeo Vegio's Latin Supplementum (1428): Twyne brings a similar kind of closure to Phaer's project to that involved in Vegio's Aeneid Book 13.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.051
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.019
Scholarly communication0.0060.004
Open science0.0000.003
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.021
GPT teacher head0.216
Teacher spread0.195 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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