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Record W3094796286

Immortal Words: the Language and Style of the Contemporary Italian Undead-Romance Novel

2018· dissertation· W3094796286 on OpenAlexaff
Christina Vani

Bibliographic record

VenueTSpace · 2018
Typedissertation
Language
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRomanceStyle (visual arts)LiteratureArtLinguisticsPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

This thesis explores the language and style of six “undead romances” by four contemporary Italian women authors. I begin by defining the undead romance, trace its roots across horror and romance genres, and examine the subgenres under the horror-romance umbrella. The Trilogia di Mirta-Luna by Chiara Palazzolo features a 19-year-old sentient zombie as the protagonist: upon waking from death, Mirta-Luna searches the Subasio region for her love… but also for human flesh. These novels present a unique interpretation of the contemporary “vampire romance” subgenre, as they employ a style influenced by Palazzolo’s American and British literary idols, including Cormac McCarthy’s dialogic style, but they also contain significant lexical traces of the Cannibali and their contemporaries. The final three works from the A cena col vampiro series are Moonlight rainbow by Violet Folgorata, Raining stars by Michaela Dooley, and Porcaccia, un vampiro! by Giusy De Nicolo. The first two are fan-fiction works inspired by Stephenie Meyer’s Twilight Saga, while the last is an original queer vampire romance. These novels exhibit linguistic and stylistic traits in stark contrast with the Trilogia’s, though Porcaccia has more in common with Mirta-Luna than first meets the eye. While the former two are set in the United Kingdom and feature simple dialogue with scant reflections of realistic speech, the latter unfolds in Apulia and reflects the idiomatic expressions and linguistic conventions typical of Italian youth—and young Italian writers. In my thesis, I demonstrate that these works display characteristics that are strictly Italian rather than merely featuring a mirror image of American art lacquered in green, white, and red. While they model themselves on and are inspired by the long and rich history of vampire literature in anglophone cultures, these undead romances, written in Italian for local audiences, betray a burgeoning interest in darker genres in Italy. Questa tesi esplora la lingua e lo stile di sei romanzi rosa che hanno come protagonisti i vampiri o gli zombie, scritti da quattro scrittrici italiane tra gli anni 2005 e 2010. Innanzitutto, offro una definizione dell' "undead romance", il nome che uso per definire questo sottogenere di romanzi rosa sui vampiri e gli zombie. I libri studiati sono i seguenti: Non mi uccidere (2005), Strappami il cuore (2006) e Ti porterò nel sangue (2007) di Chiara Palazzolo; Moonlight rainbow (2009) di Violet Folgorata, nom de plume di Monica Montanari; Raining stars (2010) di Michaela Dooley, nom de plume di Michaela Intermite; Porcaccia, un vampiro! (2009), ripubblicato come Echi di sangue, di Giusy De Nicolo. Nella tesi, dimostro che in questi testi sono manifeste delle caratteristiche strettamente legate alla cultura italiana e che questi testi, anche se influenzate dalla cultura anglosassone che ha dato luce a questo sottogenere, questi romanzi sono, difatti, italiani. Anche se l'undead romance risale a radici anglofone e angloamericane, l'undead romance italiano, scritto in italiano per un pubblico italiano di ragazze e donne, non è semplicemente una versione tradotta di romanzi che fanno parte di un genere già stabilito da secoli nel mondo anglosassone.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.006
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.009
Scholarly communication0.0060.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.035
GPT teacher head0.308
Teacher spread0.273 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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