Bibliographic record
Abstract
The article examines the phenomenon of the rapid formation of artificial intelligence (AI) as an object and means of state policy in Russia and other countries. The information basis for the study of this phenomenon is abundant and contradictory. Every day, there is a lot of fragmentary information in various domestic and foreign media about private facts and statements by leaders of different countries about the role of AI in their politics and correctly. In this regard, the main attention was paid to several dozen official documents, which made it possible to compile generalized ideas about the essence and features of considering AI as a subject and means of public policy in Russia and abroad. To study such documents, methods of systematic diagnostics of socio-political and socio-economic processes and phenomena developed and tested at the Federal Research Center “Informatics and Management” of the Russian Academy of Sciences were used. It was shown that at present, government strategies and programs for the development of AI have been adopted and are being implemented in the world, reflecting various state interests in this subject. It is also shown that in many cases, states claim to be world leaders in the development and use of AI. Attention is drawn to attempts to use AI for public administration purposes as a means of total control over public behavior and loyalty of citizens. The assumptions about the long-term continuation of this policy are substantiated.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".