Cultural translation: An introduction to the problem, and Responses
Bibliographic record
Abstract
Etymologically, translation evokes an act of moving or carrying across from one place or position to another, or of changing from one state of things to another. This does not apply only to the words of different languages, but also to human beings and their most important properties. They too can be moved across all sorts of differences and borders and so translated from one place to another, for instance from one cultural and political condition to another. Thus, one can culturally translate people – for a political purpose and with existential consequences. No discussion of the concept of cultural translation can easily dispense with an analysis of the very concrete devices of such translation if it strives to maintain contact with the political and existential issues at stake in the debate on cultural translation. The political meaning of cultural translation is not a quality external to the concept and capable of being discussed in a haphazard way. Precisely by becoming cultural, translation opens up the problem of its intrinsic political meaning.
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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 | 0.039 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.015 | 0.041 |
| Scholarly communication | 0.022 | 0.034 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.023 | 0.026 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".