Bibliographic record
Abstract
Abstract Studies of news translation and indirect translation have challenged classical concepts of Translation Studies, but the two subfields have taken separate paths. This article applies Assis Rosa, Pięta, and Bueno Maia’s (2017b) classification of indirect translation to data collected via workplace studies conducted in two multilingual news agencies based in Switzerland and one monolingual broadcaster based in Canada. Illustrative examples are provided of the first six types of (in)direct translation in the classification. This typology allows for the inclusion of phenomena that may have been previously disregarded as translation, such as oral mediations and transfers from public-relations agencies to news agencies and other media outlets. However, news translation is a borderline case of translation that pushes Assis Rosa, Pięta, and Bueno Maia’s (2017b) classification to its limits because of the centrality of reported speech in news stories. Indirect translation seems to be able to bridge various subfields of Translation Studies.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".