Bibliographic record
Abstract
Anita Desai’s latest story collection, The Artist of Disappearance, includes a novella titled “Translator, Translated.” In it, a naïve young woman begs a former classmate, who now runs a publishing house, to give her the chance to render a beloved Oriya author into English: “She is such a great writer and no one here even knows her name. It is very sad but I am sure if you publish a translation of her work, she will become as well-known as – as – Simone de Beauvoir!” (Desai 2011, 58). It is no accident that the great feminist theorist is referenced here; gender and translation have long been closely linked. Translation makes it possible for us finally to see the previously invisible, hear the previously unheard, countering at least some of the effects of linguistic, cultural and gendered obscurity, but these acts of transmission or transcreation are often problematic. Important questions need to be addressed: who chooses what gets translated? Into which languages? From which languages and cultures? Who dares speaks for whom? What is my own complicity? This paper will briefly discuss some very different examples of my work in the area of “women in translation”, such as helping bring to light previously unknown women’s voices from India’s Orissa province, suggesting non-existing readings that (if only they did exist) might have allowed women’s silence to be broken in inspiring ways, and bearing witness to the great range of responses to Classical Japanese women’s writing through exploration of its highly complex Western translation history.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".