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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".