Analysis of Intrabranch and Legal Regulation of Artificial Intelligence Technologies Using the Example of International Experience, the Experience of Foreign Countries and the Russian Federation
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 purpose of the study is to analyze intrabranch and legal regulating relations related to the development and application of artificial intelligence technologies. The documents of the strategic development of the industry, regulatory documents, and other documents directly and indirectly related to artificial intelligence technologies were studied. For example, the following are: the act of the Asilomar Conference, acts of the Council of Europe, acts of the European Union, the act of the Organization for Economic Cooperation and Development, the G20 Act, regulatory and technical documents of the United States, China, Canada, Denmark, France, the Russian Federation, as well as some bills. The analysis revealed: the insufficiency of regulatory regulation of the artificial intelligence branch, the shortcomings of national regulation of the artificial intelligence branch in some countries, the dependence of norms on the political regime, the duration and untimeness of the development of regulations, the lack of coherence in the development and application of artificial intelligence technologies at the interstate level.
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 | 0.017 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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