Transcending the Discourse of Accuracy in the Teaching of Translation: Theoretical Deliberation and Case Study
Bibliographic record
Abstract
The practice of translation in China and Australia has been influenced by a discourse of accuracy, which requires translations to be accurate, faithful, objective and impartial, etc. In the teaching of translation, this discourse has translated into a pedagogy that is centred on rules (i.e., criteria, standards and authority), that tends to rely on mechanical drills to enforce those rules and that dis-empowers students by turning them into innocent precision instruments. In three sections, the author of this article will discuss how he has been inspired by recent critical theories to reorient his teaching out of the discourse of accuracy.
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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 | 0.064 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.027 | 0.061 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".