Bibliographic record
Abstract
The object of this working paper is the policy and priorities of the digital economy leaders (the G20 members) in regulating digital platforms. The goal of the study is to assess the impact of these countries’ policies in the field of digital platforms regulation and to develop recommendations regarding Russia’s policy on digital platforms and its participation in developing new global regulatory approaches. In order to achieve this goal, the working paper addresses the following objectives: to clarify the criteria and select the leading countries from among the G20 members on the basis of expert assessments and data from international ratings; to carry out the analysis of policies and priorities of the selected digital leaders (UK, USA, Canada, EU, Japan, Korea, India, China) as well as Russia in regulating digital platforms; to develop recommendations regarding Russia’s policy on digital platforms regulation at the national level, as well as promoting its approach within the G20 and other multilateral institutions. The study is highly relevant since the leading developed countries increase their regulatory potential, including through the consistent inclusion of their standards and cooperation norms they have developed in the documents adopted by multilateral organizations, and thus create a global market for their goods and services, and additional opportunities for their companies. The study shows that a strong potential exists for a positive effect in terms of a coordinated approach to regulating digital platforms’ activities at the international level. In this context, it is important for Russia to integrate the issues of digital platforms regulation into the BRICS and G20 digital economy agendas.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".