Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".