Should Revision Trainees Think Aloud while Revising Somebody Else’s Translation? Insights from an Empirical Study with Professionals
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.
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 | 0.022 | 0.141 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".