The concept of digital human rights: The search for new justification approaches from a comparative perspective
Bibliographic record
Abstract
The relevance of the study lied in the need to adapt the legal framework to the challenges of the digital era by defining the principles of responsible digital development and ensuring equal access to digital opportunities. The purpose of the article was a compare of the conceptions and classification of digital human rights, in particular, to determine the criteria for their classification, to analyse the right to internet access as a key digital right and to study the best practices of different countries (Canada, Estonia, Lithuania and Ukraine). The study used comparative analysis, legal analysis, documentary analysis, content analysis, descriptive method and system analysis to examine the concept and classification of digital human rights, their practical implementation and impact on the realisation of other fundamental rights. Unclear criteria for classifying digital rights as fundamental make it difficult to develop international legal norms for a secure and democratic digital future. The study emphasised the importance of internet access as a key right that facilitates the realisation of other digital rights and reduces digital inequality. An analysis of the practices of countries with developed infrastructure and legislation showed that effective digital transformation reduces access-related discrimination and restrictions on rights in the offline environment. The practical significance lies in the formulation of recommendations for improving the legal regulation of digital rights and ensuring universal access to the internet as a key tool for social equality and development
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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 | 0.021 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.006 | 0.085 |
| Scholarly communication | 0.015 | 0.032 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".