Bibliographic record
Abstract
Linguistic theory and translation theory both deal with language; however, they rarely meet or use each other's results in order to advance their individual areas of research.Linguists often seem to look at translation as either trade or art rather than science, and translators show cynicism about linguistic inquiry ignoring real language data.This paper focuses on one particular area of concern in both linguistics and translation: how to incorporate pragmatics into an explanation of which translation or interpretation is best for a given linguistic expression in a given linguistic and extra-linguistic context?Students in practical translation classes do not appreciate explanations along the lines "this is simply how you would say it in language X" or "this is what the speakers of X would say in this situation".Speakers of X are balancing their knowledge of rules and conventions of language use with pragmatic know-how; they are making choices that translators -both human and machine -are supposed to imitate in the target language context.We present several examples and discuss how Sperber and Wilson's Relevance Theory could claim translational explanatory adequacy in its handling of the "division of labour" between codal knowledge and inferencing.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.042 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".