Getting more than you paid for? Consideration in integrating free and low-cost technologies into translators training programs
Bibliographic record
Abstract
Translation technologies are now an integral part of most translator training programs, and recently, a number of free and low-cost translation tools have begun to appear on the market. Because translator training programs typically have limited budgets, such software has great appeal. However, before adopting these tools, trainers must consider a range of questions, including practical issues, such as laboratory management and language considerations, as well as more pedagogically-oriented questions, including academic priorities, market needs, and possibilities for a wider integration of technologies into translation programs. This paper will explore such questions, and will introduce the Collection of Electronic Resources in Translation Technologies (CERTT) Project, discussing ways in which CERTT could potentially help to maximize the benefits of incorporating free and low-cost software into translator training.
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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 | 0.105 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".