When does digital democracy work? How policy domains shape government responses to online petitions in Taiwan
Bibliographic record
Abstract
Governments around the world have implemented digital channels of participation in recent years, but the policy effects of these new forms of participatory democracy remain unclear. Focusing on a well-known online petition platform in Taiwan, I ask: when, and under what conditions, can citizens influence the government’s policymaking process through digital democracy initiatives? By analyzing a dataset of 262 petitions, conducting interviews with government officials and citizens, and examining publicly available documents and meeting transcripts, I argue that the policy domains in which petitions are situated significantly shape policy outcomes. Specifically, citizens are more likely to succeed if their demands are located within relatively ‘open’ policy domains – those characterized by the constant emergence of new issues – or within ‘collaborative’ policy domains, where major interest groups share a common policy agenda with the government. Conversely, in ‘adversarial’ domains marked by conflicts between the government and social groups, or in ‘technocratic’ domains where decision-making is dominated by experts, citizens are unlikely to influence policies even within the context of innovative digital initiatives. These findings illuminate the potential and constraints in novel forms of digital governance, contributing to the research on government responsiveness and participatory democracy in the digital age.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".