Assessing the Status of Technical Documents as Textual Materials for Translation Training in Terms of Technical Terms
Bibliographic record
Abstract
In this paper, we examine how methods for evaluating corpora in terms of technical terms can be used for characterising technical documents used as textual materials in translation training in a translation education setup. Technical documents are one of the standard types of textual materials used in translation training courses, and choosing suitable materials for learners is an important issue. In technical documents, technical terms play an essential role. Assessing how terms are used in these documents, therefore, would help translation teachers to choose relevant documents as training materials. As corpus-characterisation methods, we used self-referring measurement of the occurrence of terminology and measurement of the characteristic semantic scale of terms. To examine the practical applicability of these methods to assessing technical documents, we prepared a total of 12 short English texts from the six domains of law, medicine, politics, physics, technology and philosophy (two texts were chosen from each domain), whose lengths ranged from 300 to 1,150 words. We manually extracted terms from each text, and using those terms, we evaluated the nature and status of the textual materials. The analysis shows that even for short texts, the corpus-characterisation methods we provide useful insights into assessing textual materials.
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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 | 0.026 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".