Citizen Empowerment in the Digital Era: Redefining Administrative Legitimacy and Power Dynamics
Bibliographic record
Abstract
ABSTRACT In an era defined by unmatched digital connectivity and vibrant global activism, traditional hegemonic power structures are increasingly subject to scrutiny and transformation. This article examines how technological innovations, dynamic social movements, and emergent forms of global citizenship are reshaping public administration. These converging forces enhance transparency, accountability, and citizen participation, while simultaneously challenging conventional bureaucratic models and established notions of administrative legitimacy. Social movements, empowered by digital platforms, are not only contesting top‐down governance but also fostering new avenues for collective action and civic engagement. As these processes unfold, traditional administrative systems are being reimagined to accommodate the demands of a digitally empowered citizenry, creating a governance landscape that is more decentralized and responsive. This commentary highlights the complex interchange between digital governance, social mobilization, and global identity formation, and explores their implications for public policy and institutional accountability. It underlines both the potential of these forces and the risks they pose, such as exacerbating existing inequalities and power imbalances. Ultimately, the article calls for a comprehensive rethinking of public administration that adopts the benefits of this digital revolution while ensuring equity, transparency, and responsiveness in a rapidly interconnected world.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.066 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".