Bibliographic record
Abstract
The present paper discusses the results of an explorative study on the impact of translation revision procedures on the process and product of revision.The aim of this research is twofold.First, it is to establish which translation revision procedures are worth comparing in the experimental setting of the main study.Second, it is to determine whether it is opportune to allow the subjects in the planned extended experiment to revise on paper, in addition to revising on screen.Regarding the former question, it seems reasonable to compare four particular revision procedures, which are applied by professional revisers and recommended by translation scholars.As far as the latter question is concerned, it may be interesting to leave the revisers the choice.However, when it comes to the methodology of the main research -in which revision processes will be analysed through keystroke logging and think aloud protocols -, this approach has several drawbacks. Journal of Specialised Translation(http://www.jostrans.org),argues that "the revision of the work of other translators may become increasingly important, at least in Europe, with the publication in 2006 of the new standard EN 15038 Translation services -Service requirements".In the same publication, Martin (2007: 61) presents a similar argument.Finally, also Mossop (2006) explains, in an article on the effects of computerization on Isabelle ROBERT.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.513 | 0.356 |
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".