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Record W4383115631 · doi:10.7202/1100479ar

Preface translation as a feminist and inclusive language usage action

2023· article· en· W4383115631 on OpenAlexvenueno aff
Érica Lima

Bibliographic record

VenueMeta Journal des traducteurs · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender Studies in Language
Canadian institutionsnot available
Fundersnot available
KeywordsPortugueseSociologyInterpretation (philosophy)State (computer science)LinguisticsComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

This paper focuses on the translation of a collection of seventeen prefaces for different editions of the book Our Bodies, Ourselves (Chatterjee 2008/2020, translated by Nicolella, Oliveira et al. ). The translation project is part of a collective and voluntary translation agreement made between the State University of Campinas, the Federal University of Rio de Janeiro, and an NGO ( Coletivo Feminista Sexualidade e Saúde ) to translate and adapt to Brazilian Portuguese the widely known book Our Bodies, Ourselves (The Boston Women’s Health Book Collective 2011). Two points were discussed more thoroughly: first, the importance of paratexts (Genette 1987/1997) and paratranslation, especially the intersemiotic interpretation of the text and the image of the book cover (Yuste Frías 2022; 2021; 2011); and second, inclusive language use (Governo do Estado do Rio Grande do Sul 2014), considering that the masculine form is the standard in Portuguese while the translation project concerns a feminist book (Davis 2007). We present four examples of translations made by students with the support of CAT tools and revised considering a more inclusive language usage. In the final remarks, we will discuss that the translation project provided the integration between translation practice and theoretical discussions underlying this practice. We will also point out some challenges and discussions still ongoing in Brazil.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.083
GPT teacher head0.384
Teacher spread0.301 · 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 designQualitative
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
Published2023
Admission routes1
Has abstractyes

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