Official Language Minority Communities, Machine Translation, and Translator Education: Reflections on the Status Quo and Considerations for the Future
Bibliographic record
Abstract
Owing largely to budgetary pressures, official language minority communities in Canada have a great number of unmet translation needs. The use of machine translation presents the possibility of a cost-effective solution, but only if members of this community are willing to accept this form of translation. This paper reports on an experiment whereby members of one official language minority community in Canada—the Fransaskois—were surveyed to determine their level of acceptance of machine translation. Results show that while many Fransaskois are quite favourable to the possibility of using post-edited machine translation, those who are also language professionals are extremely opposed to the use of any form of machine translation. This finding prompts a reflection on whether the way in which translators are trained in the use of technology could be an underlying factor in their reaction to machine translation use, which in turn leads to a proposal for a new approach to integrating technology more fully into translator training programs.
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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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".