Framing Ethical e-Governance: A Plaidoyer for a Human-Rights based Digital Democracy Approach
Bibliographic record
Abstract
As digital platforms increasingly influence public policy and governance, integrating human rights into digital tools becomes crucial for maintaining transparency, accountability, and inclusivity. The proposed framework focuses on ethical design principles, participatory governance models, accountability mechanisms, capacity building, and continuous evaluation to ensure that digital tools promote public participation and safeguard individual rights. The implementation of the proposed human-rights based approach involves developing ethical artificial intelligence (AI) guidelines, institutionalising public consultation platforms, creating independent oversight bodies, and establishing educational programs to enhance digital literacy. This paper highlights the significant advantages of a human-rights based approach, including enhanced trust and legitimacy in digital governance, improved inclusivity, and strengthened policy responsiveness. However, challenges such as resource allocation, stakeholder resistance, and the rapid pace of technological change require innovative solutions and continuous adaptation. Future research directions are suggested to validate the effectiveness of the proposed framework and explore its adaptability across different cultural and political contexts. Empirical studies are particularly called for to assess the real-world impact of these initiatives and refine the integration of human rights into digital governance practices. By systematically incorporating human rights into ethical e-governance, this framework not only aims to protect against potential abuses but also to leverage digital tools as enablers of a more democratic, just, and participatory governance environment. This approach underscores the transformative potential of ethical e-governance, where technology serves the public good and enhances the democratic fabric of society.
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 | 0.025 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.064 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".