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Record W2625750399 · doi:10.33137/q.i..v37i2.29226

Giovanni Kreglianovich’s <i>Orazio</i>: An Exemplum of the Process of Rewriting

2018· article· en· W2625750399 on OpenAlexvenueno aff
Joanne Granata

Bibliographic record

VenueQuaderni d italianistica · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicItalian Literature and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsLiteratureIdentity (music)PoliticsTragedy (event)ArtLiterary criticismHistoryPhilosophyAestheticsLawPolitical science

Abstract

fetched live from OpenAlex

Rewriting and reinvention of previously told stories and recognizable themes build upon an established literary canon, creating new connections amongst texts, while creating increasingly hypertextual works. This article explores the nature of rewriting, the reinvention of previously existing themes within literature, and the dialectic between past, present, and future embodied within this process. The specific focus of this study is the Dalmatian author Giovanni Kreglianovich, who, with his tragedy Orazio (1797) rewrites and adapts the ancient Roman legend of the battle between the Curiatii and the Horatii to reflect the political, social, and literary changes of the late 1800s in Europe. Primary sources such as Livy’s Ab urbe condita, Aretino’s Orazia (1546), and Pierre Corneille’s Horace (1640) are compared to and contrasted with Kreglianovich’s Orazio in order to highlight the differences between these works and to bring to the fore how and why the source material was rewritten. Utilized as a vehicle to espouse contemporary concerns, Kreglianovich employed aspects of antiquity and transformed them into a means through which social and political issues pertinent to the time could be revealed. In reusing one of the most famous identity myths of ancient Rome, Kreglianovich was able to create a unique tragedy that partakes in the literary phenomenon of rewriting, while promoting patriotism, as well as a sense of identity and belonging in his fellow Dalmatian compatriots.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.914
Threshold uncertainty score0.996

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.018
GPT teacher head0.248
Teacher spread0.229 · 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 teacher head, not a consensus.

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
Published2018
Admission routes1
Has abstractyes

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