Bibliographic record
Abstract
In this article I will examine the ways in which the ethical gestures available to translators are inscribed in the etymologies of key terms and cognate pairs (especially in English and French) within the semantic field marked out by the category of translation: trade, transfer, transgress. translate / translater, traduire / traduce, betray / trahir. What emerges is a pattern dominated by themes of give and take, loss and gain, and above all, faithfulness and betrayal. Betrayal (like the French verb trahir) holds a pivotal position within this set, due to its two-faced character, given to both deceit and revelation. 
 
 Juxtaposed on and rooted in these themes are the timeworn types in which translators have been chronically cast (when not simply ignored): the loser (mainly in the sense of the agent of loss) and the traitor. Such associations throw into stark relief the intrinsically political and ethical nature of the act of translation, which Lawrence Venuti and others have forcefully theorized and which the fate of translators in Iraq and Afghanistan, for example, have brutally embodied in recent times. 
 
 Drawing in part on my own thirty odd years as a translator of literary and non-literary texts, I will consider the implications of the figure of the translator as “double-agent” in the Canadian context, where a translation economy has grown against a backdrop of conflicts over loyalties and faithlessness. Furthermore, by way of dialoguing with Venuti’s project of “minoritizing translation,” I hypothesize a strategy of translators voluntarily affirming their “double-agency” or “traitorhood” as an additional challenge to prevailing textual and cultural assumptions and regimes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".