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Record W7133591078

Poor Things: Finding Bella’s Voice in Translation

2024· other· en· W7133591078 on OpenAlexaboutno aff
Gema Castillo Membrive

Bibliographic record

VenueUNED repository · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsIntentionalityTextualitySource textCohesion (chemistry)Representation (politics)Translation studiesPragmaticsDiscourse analysisParaphrase
DOInot available

Abstract

fetched live from OpenAlex

Alasdair Gray’s reception in Spain has been limited, but interest is growing, partly because of the recent film adaptation of Poor Things by Giorgos Lanthimos. This paper aims to explore whether the Spanish translation of Poor Things demonstrates gender awareness or sensitivities comparable to the source text, or if it instead reflects instances of sexist translation decisions and manipulations, and what underlies these translation strategies and proposals. To answer these questions, a preliminary analysis of the source text was completed following Beaugrande and Dressler’s seven standards of textuality (2002), examining cohesion and coherence, intentionality and acceptance, informativity and situationality, and intertextuality. Then, key theoretical frameworks on the crossroads of gender and translation studies were revisited and applied, going back to what has been known as “the Canadian School” and getting back to recent local scholarly work in Spain. For the selected case study, this involved exploring how men talk about woman and how women talk. An essential concept in this study is the examination of Bella/Victoria's female voice as a case of "female writing”. Bella demonstrates a highly creative and innovative use of language, which undergoes four distinct phases in the text. As for describing the translation samples selected, Molina and Hurtado (2002) categorizations of translation techniques were used. Several sexist manipulations were observed in the text, including women’s invisivilization and undervalue, the incorporation of sexist stereotypes and an inconsistent and erroneous representation of Bella/Victoria. Furthermore, Bella/Victoria’s voice Spanish lacks the independent and subversive spirit of the source text.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.051
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.011

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 head0.017
GPT teacher head0.261
Teacher spread0.244 · 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
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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Same venueUNED repositoryFrench-language works237,207