Giovanni Kreglianovich’s <i>Orazio</i>: An Exemplum of the Process of Rewriting
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".