CChallenges and (partial) solutions of gender-inclusive translation: Spanish, English, and French in the context of participatory research
Bibliographic record
Abstract
Both co-authors have been coordinating translation work for the participatory research project The Surviving Memory in Postwar El Salvador including a variety of audiovisual materials (websites, ethics documents, subtitling videos, etc.). This work presents our observations of student translator training based on mutual education as an effort to negotiate across differences (regional, generational, linguistic, etc.) and to model language that is less (gender) exclusionary while also honoring cultural and historical specificities. After presenting the participatory research project and describing the different types of translation tasks, we consider some concrete cases of translations towards Spanish but also in some cases towards English and French, along with theoretical and practical implications. The extracts that we discuss come from the project's Governance Model, followed by some examples of translations from survivor testimonies, Community dissemination reports, ethic protocols and other administrative documents. Throughout the discussion of these examples, we reflect about our challenges in translation, mostly linked to learning and teaching how to translate while translating and also about Inclusive Translation as (mutual) education. The discussion includes some thoughts about the challenges of training in our collaborative approach to translation, translating with space limitations, and the use (or not) of nonbinary inclusive language in translation, all of which contribute to our vision of translation as mutual education.
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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 | 0.049 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.036 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".