Bibliographic record
Abstract
On prétend que les noms propres ne se traduisent pas. Ceci est loin d’être le cas des noms d’institutions, qui comportent souvent un équivalent très officiel dans plusieurs langues étrangères, mais il ne semble pas exister une doctrine établie concernant les principes qui doivent présider à ce genre de traduction. La présente étude vise à proposer quelques principes à la lumière de l’observation de la pratique actuelle, telle qu’on la constate en Europe en particulier. À partir d’Internet, qui sert de mégacorpus, on a examiné des traductions de noms d’institutions (politiques, économiques et d’enseignement) sur les plans international et européen, national, régional (et départemental pour la France) et local. Le plan international semble le plus systématique, le plan local le moins en ce qui concerne la présence de la traduction et de sa forme. On propose une série de recommandations à l’intention d’institutions désireuses de traduire leur nom.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".