Bibliographic record
Abstract
Depuis 2013 et les révélations d’Edward Snowden, la problématique de la souveraineté numérique est devenue un enjeu central en Russie. Selon ce nouveau paradigme, le gouvernement russe a lancé une politique de promotion du développement logiciel dans le pays, appuyée par les acteurs privés qui en bénéficient au premier chef. Une stratégie de russification des logiciels employés sur le territoire semble également avoir émergé, sous la forme de nouvelles normes visant à réglementer la conception, le fonctionnement et l’usage des outils informatiques. Ce passage de l’industrie numérique à une position majeure (à valeur stratégique) en Russie, dans le but d’assurer à la fois les intérêts de l’État et des entreprises russes sur le territoire, mais aussi en-dehors, peut ainsi amener à se demander si le pays compte désormais parmi les puissances internationales du numérique : la Russie est-elle devenue une cyber-puissance ?
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".