Luso-Canadian Exchanges in Translation Studies: Translating Linguistic Variation
Bibliographic record
Abstract
“Translation scholars no doubt can learn much from scholars of ethnic minorities, women, minor literatures and popular literatures. Much of the most exciting work in the field is already being produced by scholars from the “smaller” countries – Belgium, the Netherlands, Israel, Czechoslovakia, and French-speaking Canada” (Gentzler 2001: 197). Several Canadian scholars have been very influential in Translation Studies. The main aim of this collaborative paper on Luso-Canadian exchanges in TS is to make a very brief presentation of how some of the most “exciting” work by Canadian scholars has been received, adopted, adapted and developed in research work and teaching by Portuguese TS scholars. Selected examples of theoretical and methodological proposals by Canadian researchers in TS will be discussed, a few studies by Portuguese scholars will be mentioned, and the operative application of these studies to translation practice and teaching will be illustrated by the presentation and analysis of short excerpts of English narrative source texts, followed by their target texts in Portuguese, as produced and commented upon by former students of the Department of English, Faculty of Letters University of Lisbon.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.024 | 0.016 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".