Human Rights and Freedoms in the Digital Era: Problems and Perspectives of Their Establishment in the Eurasian Space
Bibliographic record
Abstract
The article touches upon the process of transformation of a socio-political organization in the legal system, caused by integration of digital technologies and autonomous algorithmic systems. It is justified that the development of machine education and artificial intelligence systems can result in human suspension from taking important social and political decisions. Impermeability and fluidity of modern digital technologies provides hidden functioning mode of the latter, whilst their specificity is not included in the formulated agenda, creating and taking managerial decisions or planning everyday life activity. The article proves that the aftereffects of the fourth technological revolution claim for returning of the human rights standards, freedoms and legal interests as the leading form of a reference point in the global digital rivalry and in the development of certain social systems, also the necessity to establish a distinct system of human rights guarantee and human safety in the era of developing principally new digital relations is proved. Meanwhile, digital information legislation needs systematization and a strict consistent state.
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.
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.046 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".