Interaction Between Gender and Translation (1995-2022): A Systematic Literature Review
Bibliographic record
Abstract
Although the link between gender and translation has been recognized and reaffirmed, research on the interaction between them remains fragmented. This study aims to examine the research foci of the interaction between gender and translation. Specifically, it brings these insights together to identify opportunities for future research. A systematic literature review was conducted based on the PRISMA 2020 statement. This review includes 50 journal articles indexed in Web of Science (WoS) and Scopus databases ranging from 1995 to 2022. The synthesis identifies five main topics of the impact gender has on translation: (a) gender affiliation of translators/interpreters; (b) LGBTQI (lesbian, gay, bisexual, transgender and queer) identities; (c) sexuality of characters; (d) feminist and patriarchal ideologies; and (e) cultural gender conventions, as well as four topics of the impact of translation on gender: (a) gender (stereotypical) portrayals of fictional characters; (b) gendered language and meanings; (c) LGBTQI identities; and (d) LGBTQI ideologies. The results support the interplay of translation and gender issues. While the impact of translation on gender and vice versa is largely constant across studies, there are differences in how translation affects gender and vice versa. Moreover, various reasons have been highlighted to elucidate the intricacy of the interaction. This review also offers suggestions for further research on this topic, as well as potentially provides implications for interdisciplinary translation studies.
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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 | 0.043 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.032 | 0.036 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".