Bibliographic record
Abstract
This paper reflects on the terms used in investigating news translation, with a special focus on the term transediting as it was suggested in a paper by Stetting. After a summary of Stetting’s original arguments, some research into news translation is presented, illustrating main methods, findings, and concepts used. The paper presents arguments put forward by various scholars for using or rejecting the term translation for describing the complex processes of translation in the context of mass media and illustrates which alternative terms are used. It is shown that Stetting’s original aim in coining the term transediting was to raise awareness of translation being more than a pure replacement of a source text by an equivalent target text. Transformations as identified in news translation, however, are characteristic of translation more generally. Therefore, the paper finally reflects on whether there is a need to keep the term transediting and whether it has any explanatory power for describing the practices in news translation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.020 | 0.032 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".