Bibliographic record
Abstract
Dans un ouvrage récent (D’hulst 2014), j’ai plaidé pour une histoire de la traduction moins préoccupée par son positionnement au sein de la traductologie ou en bordure d’autres pratiques savantes prêtes à lui ménager une place (l’histoire culturelle et sociale, l’histoire des sciences, de la philosophie, de la littérature, de la linguistique, etc.) que par la spécificité et par la valeur du point de vue historiographique sur la traduction. Il s’agirait corrélativement de définir les objets et les méthodes au service de l’étude historique de ces derniers, en dialogue avec les savoirs et disciplines qui gravitent autour de celle-ci et lui procurent des concepts et des modélisations. Il s’agirait aussi de montrer la signification sinon l’importance des recherches historiques sur les traductions et les savoirs traductifs au regard d’autres activités intellectuelles et sociales. Ces différents défis forment l’objet de cette contribution ; elle s’appuiera sur des exemples puisés dans un éventail de domaines, périodes et aires culturelles.
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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.068 |
| Scholarly communication | 0.026 | 0.034 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 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".