Bibliographic record
Abstract
En dehors d’exemples bien connus (mais insuffisamment étudiés d’un point de vue proprement linguistique) de transcriptions quasi phonétiques ( Popoff ) et de translittérations ( Popov ), les pratiques existantes pour rendre les noms propres russes en français présentent plusieurs cas intermédiaires qui n’ont pas été systématisés ni décrits dans des termes linguistiques stricts. Nous proposons une classification de travail basée sur des données trouvées sur des sites web français concernant les variations des graphies de certains noms tels que Gorbatchev, Eltsine, Ekaterinbourg . Parmi d’autres questions abordées, notons les difficultés liées au système anthroponymique russe (patronymes, diminutifs, traduction de noms « parlants », connotés, dans des textes littéraires) et celles qui sont la conséquence des évolutions politiques récentes dans l’espace post-soviétique (débaptisations, rebaptisations, autochtonisation toponymiques).
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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".