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Record W4402652134 · doi:10.4324/9781003184454-37

Corpora, Translation, and Gender

2024· book-chapter· en· W4402652134 on OpenAlexaboutno aff
Luciana Carvalho Fonseca

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsTranslation (biology)LinguisticsNatural language processingComputer scienceArtificial intelligencePhilosophyBiology

Abstract

fetched live from OpenAlex

Corpus linguistics (CL) has been regularly used to analyse language and gender. There are corpus studies of gender and difference, gender and language, gender and discourse, gender and representation, gender and terminology, and so on. With regard to translation and gender, the amplitude of fields and objects of study is even wider and encompasses theoretical, descriptive, and applied translation studies (TS). However, the scholarly application of CL to translation and gender remains a marginal field of research. Where translation studies and gender studies meet, therein lies feminist translation, which was identified as a specific subfield of TS in the 1970s in Quebec. More than 50 years later, feminist translation – a field that embraces gender and translation, women and translation, and so on – has been practised and theorised by translation scholars across the globe from multiple points of view. To investigate and discuss how corpora, translation, and gender have been brought together by translation scholars, this chapter begins with a brief account of the interrelation between corpus and translation, corpus and gender, and translation and gender. It then moves on to address how feminist translation has unfolded and has relied predominantly on ‘hand and eye’ methods, thus indicating that corpus methods and tools have been used only very tentatively as a methodological approach in the field. This chapter addresses three research questions based on a literature review of feminist translation publications: (1) Which papers and chapters in feminist translation publications have employed corpus methods? (2) How have corpus methods been applied in these works? (3) What are the trends identified and insights drawn from corpus-informed studies of feminist translation? The review of the literature consists of ten papers and chapters that employed corpus methods to analyse feminism/gender/women and translation. The ten papers were obtained from 40 (23 collections and 17 special issues) publications on feminism/gender/women and translation.

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)0.025
metaresearch head score (Gemma)0.069
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 score0.025
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.069
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.016
Science and technology studies0.0070.010
Scholarly communication0.0140.011
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0220.005

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 head0.197
GPT teacher head0.277
Teacher spread0.080 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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