Coding Betweenness in Swedish and Norwegian Translations from English
Bibliographic record
Abstract
This paper examines the way in which the semantic notion of ‘betweenness’ is coded in Swedish and Norwegian translations of the same English source texts. The study takes its starting point in the contention that the original English expressions of ‘betweenness’ containing the preposition between constitute a viable tertium comparationis for translations of that form into the other two languages. A classification of all occurrences of between in the English source texts in The English-Swedish Parallel Corpus (ESPC) and The English-Norwegian Parallel Corpus (ENPC) in terms of the semantic role of the landmarks in the predications is followed by an analysis of the translations, both congruent and divergent. The primary focus, however, is not on the correspondences between the English original and its translations into Swedish and Norwegian, but on the parallels between the two sets of translations. To this end comparisons are drawn between the Swedish and Norwegian renderings of the various meanings of between in the source data. The analysis shows that Swedish and Norwegian resemble one another closely in the means employed to code the various senses of between . The last part of the study offers a complementary perspective in comparing occurrences of the most common translation equivalents of between , mellan in Swedish and mellom in Norwegian, in contexts where they do not translate between in the English source texts. This approach reveals that, despite the lack of between in the original texts, the two sets of translators both employ the cognate prepositions in over 25% of cases.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".