Terminological Variation in Source Texts and Translations: A Pilot Study
Bibliographic record
Abstract
In this article, it is assumed that the choice of terminological variants in specialized source texts is sometimes cognitively motivated and that this motivation is reflected in the choice of equivalents in the target texts. On the basis of a pilot study, we will present a method for comparing the cognitively motivated terminological variants in source texts and their translations. The corpus in the pilot study is composed of three Galician source texts and their English translations. The texts are scientific articles addressing the economic effects of environmental disasters on fisheries. A quantitative study was first carried out in which the number of unique terms in each source text was compared to the number of unique translations of these terms. Next, each unique combination of a source term and its translation equivalent was subjected to a qualitative analysis. A value was manually assigned in order to qualify the “cognitive distance” between the source term and its translation. Based on these values and the frequency of the translation pair in each bitext, we computed the “interlingual variation index” (IVI). Differences in results between the bitexts are linked to extra-linguistic factors related to the translation processes.
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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 | 0.017 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".