Transformation of the Socio-Political Sphere in Digital Era: Some Problems of Value-Normative Harmonization
Bibliographic record
Abstract
The article problematizes the functioning, on the one hand, of traditional value-normative regulators, ensuring socio-cultural integrity and social and political dynamics, and on the other, the development of standards for the development and application of digital algorithms, autonomous robotic systems and artificial intelligence. It discusses various approaches and theoretical and practical positions that argue and model the development of a socio-political organization in the digital era, and describe fundamental changes in political communication, law and order, public-power interaction, etc. The authors argue that the introduction of digital algorithms and robotic technologies radically changes the basic sociocultural meanings and the socio-political landscape, the formation of new types of social relations, where people, things, machines together form special modes of functioning, specific relations. The work proves that these cardinal changes require fundamentally new theoretical and methodological approaches to research, and new tools for political modeling and forecasting. The problems of legislative and ethical regulation of the processes of informatization and technological development of the society are discussed separately. The final part of the research shows that modernity poses the fundamental problem of correlating digital and socio-cultural trends in the development of political space, their contradictions and the inevitable convergence of socio-cultural, traditional socio-political dominants of development with digital trends and life forms. The article proves that harmonization of existing value-normative regulators and standards of digital algorithms and artificial intelligence systems being developed is necessary, since the latter can be a key guarantee for the formation of an effective system of social system development and its stability in the digital age.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.091 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.009 |
| 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".