Corpus-Based Applications for Translator Training: Exploring the Possibilities
Bibliographic record
Abstract
This article explores a number of possible corpus-based applications in translator training. The first involves the use of corpora created by translators (CCBT), a type of learner corpora that can be used to investigate difficulties encountered by trainee translators. The second focuses on the use of corpora created for translators (CCFT), monolingual target-language reference corpora that can be used as a resource for finding translation equivalents at a number of levels, including lexical, phraseological, syntactic, and stylistic. Finally, comparable corpora (CC), which consist of translations into a given language alongside similar texts that have been originally written in that same language, are considered. These CC can be created by combining CCBT and CCFT, and they can be used as evaluation corpora to help trainers provide feedback on student translations, or as a body of data for investigating the nature of translated text as compared to original language text.
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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 | 0.032 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".