Participatory Democracy in the Digital Age: Opportunities and Risks for Political Engagement
Bibliographic record
Abstract
This scholarly article investigates the profound influence of digital technologies on participatory democracy, evaluating both the prospects they offer and the hazards they pose for civic engagement. While digital platforms enable an unprecedented level of citizen participation in democratic mechanisms, they also introduce complexities such as misinformation dissemination, digital disparities, and the potential undermining of democratic principles. Through an examination of theoretical constructs and empirical case analyses, this research endeavours to deliver a holistic comprehension of how digital instruments are reconfiguring democratic involvement and to suggest approaches for addressing associated challenges. Introduction: The emergence of digital technologies has markedly reshaped the terrain of democratic participation. Platforms including social media, e-participation mechanisms, and AI-supported systems have empowered citizens to interact more directly and consistently with political processes. Nevertheless, these innovations also provoke apprehensions concerning misinformation proliferation, digital divides, and the calibre of deliberative democracy. This paper aims to critically assess the dichotomous nature of digital participatory democracy, accentuating both its possibilities and its limitations.
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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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.022 | 0.028 |
| Open science | 0.001 | 0.021 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".