AI-Driven Innovation in Russian Youth Policy: Strategies, Mechanisms, and Practices
Bibliographic record
Abstract
How Artificial Intelligence (AI) enhances the effectiveness of Russian youth policy implementation amidst technological advancements and digital transformation? The study’s novelty lies in its comprehensive analysis of specific mechanisms for integrating AI into the Russian youth policy system, considering national strategic priorities. Furthermore, it identifies personalized approaches to youth human capital management through AI. Analyzing the functional potential of AI technologies, the Russian Youth Policy Strategy to 2030, and relevant practices of applying digital technologies with AI systems in the context of youth policy, the authors highlight three key areas for AI implementation: 1) developing strategic monitoring and forecasting systems for youth vulnerabilities, 2) acceleration of transformation processes in the sphere of implementation of youth policy through the introduction of digital products with elements of artificial intelligence, and 3) optimizing processes for engaging youth in social dynamics, intensification of civic engagement. The article presents examples of successful national and international scenarios in these areas and proposes new approaches to enhance youth policy strategy implementation through innovative intelligent technologies. Significant limitations of AI application are noted, including ethical concerns and methodological challenges. The study outlines key risks in developing legislative initiatives aimed at regulating the use of AI within the youth human capital management ecosystem, emphasizing the importance of balancing innovation promotion with the protection of citizens’ rights and freedoms in the digital environment.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".