Artificial intelligence as a subject and means of public policy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it