The GREVIS Project: Revise or Court Calamity
Bibliographic record
Abstract
GREVIS (Groupe de recherche en révision humaine) aimed to set up an accelerated method of revising while improving the quality of the operation. The project had a three fold objective: to strengthen the place of revision in the field of translation studies, to increase revisers' satisfaction and to help the translation industry. The hypothesis of this study was that monolingual revision was just as effective as bilingual revision, and could be done at a lower cost, because it is less time-consuming. However, the results of the study disproved this hypothesis: bilingual revision was more than twice as effective as monolingual revision. The 19,407-word corpus comprised translations from the E?F pair (translated and revised in Canada) and from the F?E pair (translated and revised in the United States). Each sub-corpus (E?F and F?E) was analyzed by a team of scholars and/or revisers, according to Louise Brunette's (1997) revision criteria: accuracy, readability, appropriateness and linguistic coding. The study looked at the number of corrections, omissions and revisor-injected errors, in relation to these four criteria.
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 | 0.027 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".