Decentralism, AI, and Power Structures of Sustainable E-Governance: How Emerging Technologies Influence Global Social Justice Movements
Bibliographic record
Abstract
This article examines how decentralization and artificial intelligence (AI) are reshaping power relations in global social-justice movements. Drawing on interdisciplinary research from international relations, communication, and governance studies, it explores how digital infrastructures—from decentralized social networks and blockchain systems to platformized AI environments—redistribute and sometimes recentralize authority. AI extends surveillance, persuasion, and predictive control while enabling new forms of resistance, including algorithm-aware mobilization and multilingual coordination. Evidence shows that decentralization is not inherently emancipatory; power often re-accumulates at infrastructural chokepoints such as validators, relays, and platform administrators. Integrating insights from global case studies and North American policy frameworks, the article develops a four-pillar model addressing infrastructural power, communicative visibility, participation and data justice, and accountability for AI footprints and harms. It concludes that embedding transparency and participation in technology design is essential to ensure decentralization advances equity rather than hierarchy. This is the first interdisciplinary framework connecting decentralization, AI, Sustainable e-Governance, and power structures to global social-justice movements in international environments.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".