Bibliographic record
Abstract
Revision by a second translator plays a major role in quality control in institutional settings such as the European Commission and the United Nations. Different institutional translation services take different approaches to revision, and their approach changes over time. There are policy differences and there are differences among revisers in revision technique. Is there a best way to approach the task, a way which is as fast as possible while achieving adequate quality? Or does it depend on differing institutional requirements and budgets, different conceptions of quality, differences in staff size, and differences in the way individuals process language? If there is no best way of revising, are there ways that do not work well? Surveys of research on revision by a second translator have led to findings which, while interesting, are mostly not of a kind that, if pursued, could provide managers and revisers with a scientific basis for adopting a revision policy or recommending a revision technique. Given a certain concept of Applied Translation Studies, it should be possible, with cooperation and funding from the big translating institutions, for teams of researchers and practitioners to test hypotheses about which of a pair of techniques or policies is best.
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 | 0.185 | 0.314 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.026 | 0.057 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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".