Bibliographic record
Abstract
As a synonym for “revision,” dictionaries of different European languages include such terms as rilettura, relecture, relectura and rereading. The concept of “rereading” is also used by most translators and revisers when having to describe the process of revising a translation. This act of rereading, however, takes on different forms and purposes depending on the agent by whom it is performed, that is, the translator of the text or the reviser of the translation. As a matter of fact, revision is a second, further reading for the translator who has been working on his/her translation, but it is a “new” reading for the reviser, who approaches the translated text for the first time and, because of his/her “new vision,” can provide different insights on the work done by the translator and spot any weaknesses it may have. Drawing on current research in the field of revision as well as on first-hand data on professional revision in the publishing sector, this work aims at highlighting the peculiarities of revision as rereading when performed by translators and revisers as well as analyzing the latter’s different modes of execution, strategies, purposes and products.
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 | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".