Bibliographic record
Abstract
This paper focuses on the translation of a collection of seventeen prefaces for different editions of the book Our Bodies, Ourselves (Chatterjee 2008/2020, translated by Nicolella, Oliveira et al. ). The translation project is part of a collective and voluntary translation agreement made between the State University of Campinas, the Federal University of Rio de Janeiro, and an NGO ( Coletivo Feminista Sexualidade e Saúde ) to translate and adapt to Brazilian Portuguese the widely known book Our Bodies, Ourselves (The Boston Women’s Health Book Collective 2011). Two points were discussed more thoroughly: first, the importance of paratexts (Genette 1987/1997) and paratranslation, especially the intersemiotic interpretation of the text and the image of the book cover (Yuste Frías 2022; 2021; 2011); and second, inclusive language use (Governo do Estado do Rio Grande do Sul 2014), considering that the masculine form is the standard in Portuguese while the translation project concerns a feminist book (Davis 2007). We present four examples of translations made by students with the support of CAT tools and revised considering a more inclusive language usage. In the final remarks, we will discuss that the translation project provided the integration between translation practice and theoretical discussions underlying this practice. We will also point out some challenges and discussions still ongoing in Brazil.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".