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Record W3091674226 · doi:10.33137/q.i..v38i1.31155

The Nineteenth-Century Italian Translators of Lord Byron’s <i>Marino Faliero</i>

2018· article· en· W3091674226 on OpenAlexvenueno aff
Sergio Portelli

Bibliographic record

VenueQuaderni d italianistica · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsTragedy (event)NobilityHonourIdeologyState (computer science)Context (archaeology)HERODramaArtOligarchyLiteratureClassicsHumanitiesHistoryLawPoliticsPolitical scienceDemocracy

Abstract

fetched live from OpenAlex

The tragic story of Marino Faliero, the Doge of Venice who was executed for high treason in 1355, came to the attention of writers and artists of various European countries during the early nineteenth century thanks to a number of historians who published insightful works on the history of the Venetian Republic. Among those who were fascinated by the irascible old warrior who tried to overthrow the oligarchy on becoming head of state was Lord Byron. In 1821, the English poet published the historical drama Marino Faliero, Doge of Venice on the tragic end of a hero whose personal grievances with the Venetian Senate intertwined with an ill-fated plebeian rebellion against the nobility. Byron’s popularity in Italy brought the story to the attention of Italian romantic literary circles, where it was not only appreciated as a tragedy of honour and revenge, but also for its ideological implications in the context of the Risorgimento. This study focuses on the three translators who produced the first complete Italian versions of Byron’s play published in the nineteenth century, namely Pasquale De Virgili, Giovan Battista Cereseto, and Andrea Maffei. Based on André Lefevere’s theory on rewriting, it analyses the ideological and poetological reasons behind the translations, how the translators’ intentions shaped the target texts, as well as the impact these translations had on Italian literature and the arts. The strategies adopted by the translators are also illustrated through a comparative textual analysis of a sample passage.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.001

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 head0.030
GPT teacher head0.254
Teacher spread0.224 · 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; both teacher heads agree on what is shown here.

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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