It’s All in the Name: Redressive Translation, Passive/Active Redressive Translation
Bibliographic record
Abstract
Resistance in translation is a well known and accepted means for minorities and oppressed linguistic/cultural groups to access power, agency, and cultural repair (see Niranjana, 1992; Simon, 2005; Bandia, 2008; Cox, 2009; Tymoczko, 2010). Yet for all the good “retranslations” do, they remain yoked to a name that erases their significance by collapsing it with the simple act of “translating-again.” This paper argues for the adoption of a new term, “redressive translation,” to replace the term “retranslation” in contexts where redressive resistance in translation is manifest. It examines and defines two distinct varieties of redressive translation, namely “active redressive translation” and “passive redressive translation.” By adopting the name “redressive translation” to identify characteristics present in translated texts that resist politically and offer minority/minoritized cultures a means of healing, new clarity and strength can be brought to these translation offerings, enabling readers, researchers, translators, writers, and activists alike to share a common term for these essential articulations/manifestations of resistance.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".