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Record W7015655405

Translating Arboreality, a climate change-focused novella by Rebecca Campbell

2024· article· it· W7015655405 on OpenAlexaboutno aff

Bibliographic record

VenueElectronic Theses and Dissertations Repository (University of Pisa) · 2024
Typearticle
Languageit
FieldArts and Humanities
TopicEcocriticism and Environmental Literature
Canadian institutionsnot available
Fundersnot available
KeywordsStaringContext (archaeology)Subject (documents)
DOInot available

Abstract

fetched live from OpenAlex

L'obiettivo di questa tesi è commentare e analizzare la traduzione, dall'inglese all'italiano, della novella Arboreality di Rebecca Campbell sul cambiamento climatico, pubblicata il 29 settembre 2022 in Ontario da Stelliform Press. Il primo capitolo sarà dedicato all'analisi del libro. Dopo una breve biografia dell'autore, verrà fornito un riassunto della trama. Si passerà poi ad un approfondimento dei temi affrontati nella novella e a un'analisi dei vari personaggi. Il secondo capitolo sarà dedicato allo studio di Arboreality attraverso una prospettiva ecocritica. Inizieremo spiegando cosa si intende, da un punto di vista globale, per ecocritica e quali sono gli scopi e gli ambiti di applicazione di questo campo di studi letterari. L'attenzione sarà quindi spostata su quella che può essere definita ecocritica canadese, attraverso l'osservazione delle sue modalità culturo-specifiche di relazione tra uomo e natura. Partendo dal presupposto che l'ecocritica è una disciplina di studi letterari, ci dedicheremo, in seguito, all'analisi della fiction sul cambiamento climatico, un genere letterario in rapida ascesa, a cui appartiene Arboreality. Il terzo e ultimo capitolo sarà dedicato all'analisi della traduzione, la quale toccherà alcuni punti. Dopo aver spiegato il processo di identificazione della dominante del testo e del lettore modello, principi su cui orientare la traduzione, ci concentreremo su tre aspetti specifici: ritmo della prosa, linguaggio e repertorio lessicale. The aim of this thesis is to comment and analyse the translation, from English to Italian, of Rebecca Campbell’s climate-change focused novella Arboreality, published on September 29, 2022, in Ontario, by Stelliform Press. The first chapter will be devoted to the analysis of the book. After a brief biography of the author, a summary of the plot will be provided. We will then move on to a study of the themes addressed in the novella, as well as an analysis of the various characters. The second chapter will be devoted to the examination of Arboreality through an ecocritical perspective. We will begin by explaining what is meant, from a global point of view, by ecocriticism and what are the purposes and areas of application of this field of literary studies. The focus will then be shifted to what can be defined the Canadian ecocriticism, through the observation of its cultural-specific modes of relationship between man and nature. On the premise that ecocriticism is a discipline of literary studies, we will then shift our attention to climate change fiction, a rapidly rising literary genre, to which Arboreality belongs. The third and final chapter is devoted to the analysis of the translation, which will touch on a few points. After explaining the identification process of the dominant of the text and the model reader, tenets upon which to orient the translation, we will focus on three specific aspects: prose rhythm, language, and lexical repertoire.

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)0.003
metaresearch head score (Gemma)0.004
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: Other · Consensus signal: Other
Teacher disagreement score0.479
Threshold uncertainty score0.953

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.018
Scholarly communication0.0080.005
Open science0.0010.003
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0120.002

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 head0.010
GPT teacher head0.199
Teacher spread0.189 · 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
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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