Reading order during bilingual quality checking of translations: An issue in search of studies
Bibliographic record
Abstract
This paper investigates three questions about the order in which the source text and the translation are read during bilingual quality checking of translations. How do translators behave with respect to reading order during self-revision, other-revision and post-editing? What reasons do translators give for using one or the other order? Is one order better in terms of error detection? Reading order is of interest not only because one order may yield more error detection but also because the two orders may differ cognitively. The few existing eye-tracking, interview and survey studies on reading order are summarized and commented on. They suggest, first, that it is unclear whether reading order affects error detection. Second, that post-editors mostly look at the machine translation output first, but for self- and other-revisers, practices are mixed. Third, that as translators leave their student days behind and gain experience, about half abandon source-first as their default reading order during other-revision. Also considered are two articles involving order in other fields: metaphor studies and second language acquisition.
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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 | 0.049 | 0.208 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".