“Thematic Adaptation”. On Localizing the Language of “Global Feminism” and Gender Politics in Transnational Feminist Translation Practice and Studies
Bibliographic record
Abstract
This article reviews the linguistic and socio-cultural challenges encountered in the many different attempts by “global feminism” to mobilize concepts deriving from gender politics worldwide: the term “gender” itself and derivations such as “gender mainstreaming” are discussed. Then the article moves to questions around the translation and adaptation of “international gender talk” and cites various discussions from the realm of transnational feminist Translation Studies, a relatively new approach to the problem of communicating “gender” issues across diverse cultural and linguistic borders. It ends with a description and assessment of a recent “localizing” translation project, namely Corps Accord. Pour une sexualité positive (Montreal, Les Éditions du Remue-Ménage, 2019), an intersectional, post-colonial and transnational feminist translation into French of selected chapters from Our Bodies, Ourselves, the famed American feminist reproductive health manual from the 1970s. “Thematic adaptation” is proposed and discussed as an appropriate translation strategy for such genderfocused materials.
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 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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".