Bibliographic record
Abstract
Notre étude a eu pour objectif de réfléchir sur un sujet longtemps négligé par les traductologues malgré son importance primordiale dans tout processus de traduction, à savoir la révision. Dans cet article, il sera d’abord question de la concrétisation du concept. Il s’agira ensuite d’une réflexion sur les diverses définitions du mot qu’ont proposées plusieurs auteurs ainsi que sur les limites montrées par ces derniers quant à apporter une définition claire qui permettrait d’éviter l’écueil d’une confusion terminologique. Finalement, l’objectivité en matière de révision sera abordée, par le biais d’un examen des paramètres proposés par trois auteurs et en vue d’en produire une synthèse pour, en fait, parvenir à un total de quatre paramètres.
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 | 0.039 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".