Should Revision Trainees Think Aloud while Revising Somebody Else’s Translation? Insights from an Empirical Study with Professionals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper reported on a follow-up study whose aim was fourfold: 1) to determine which variables do seem to influence the amount of verbalization of professional revisers when they verbalize their thoughts while revising somebody else’s translation, 2) to determine what kind of revision sub-processes are verbalized, 3) to determine the relation between the type of verbalizations and revision product and process, and 4) to draw conclusions for revision didactics. Results show that variables that could have influenced the verbalization ratio of revisers had no effect on that ratio, except the revision experience. As far as verbalized subprocesses are concerned, it appeared that revisers rarely verbalized a maxim-based diagnosis, but that the more they verbalized such a problem representation, the better they detected, the better they revised, but the longer they worked. Results also show that participants who verbalized a problem representation together with a problemsolving strategy or a solution, detected better, but worked longer. Further research could focus on a particular subcompetence of the revision competence: the ability to explain.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it