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Record W7048619250

Lolita in Italia. Le riscritture letterarie del romanzo di Vladimir Nabokov

2018· dissertation· it· W7048619250 on OpenAlexaboutno aff

Bibliographic record

VenueInstitutional Research Information System (University of Udine) · 2018
Typedissertation
Languageit
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsDreamPerformance artFirst world war
DOInot available

Abstract

fetched live from OpenAlex

Questa tesi dottorale discute il fenomeno delle riscritture letterarie di "Lolita", celebre romanzo nabokoviano, in Italia e all’estero. Sebbene non vi siano studi critici che si occupino di tale tendenza, gli ipertesti (trasposizioni, parodie, pastiches, ecc) delle opere dello scrittore russo continuano ad aumentare negli ultimi anni. L’approccio metodologico impiegato è sostanzialmente quello della narratologia genettiana, con contributi esegetici di tipo psicanalitico: il mito di Lolita, infatti, è stato reinterpretato in diverse chiavi nonostante la centralità del tema della perversione.
\nIl primo capitolo del mio lavoro si sofferma sugli ipertesti della Russia, della Spagna, della Francia, degli Stati Uniti e del Canada, con un particolare sguardo alle opere statunitensi ("Colita" di J.B. Miller, "Roger Fishbite" di Emily Prager, "Lolita at Fifty" di Steve Martin) e canadesi ("Poems for Men Who Dream of Lolita" di Kim Morrissey).
\nIl secondo capitolo concerne la ricezione di Nabokov e Lolita in Italia, argomento spinoso data la decennale indifferenza della critica italiana per questo autore. In esso si esaminano quei pochi contributi di più o meno illustri recensori e interpreti, nonché le vicende editoriali legate alla pubblicazione dell’opera nabokoviana.
\nIl terzo capitolo consiste nell’analisi testuale di tre ipertesti lolitiani prodotti in Italia: "Nonita" (Umberto Eco), "Diario di Lo" (Pia Pera), "Lolito" (Daniele Luttazzi) i quali, nella loro eterogeneità, sono significativi nel dimostrare la vitalità del mito della ninfetta americana.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.586
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.263
Teacher spread0.232 · 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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