Introduction to the Special Issue ‘Beyond ‘Direct Democracy’: Popular Vote Processes in Democratic Systems’
Bibliographic record
Abstract
Despite controversy over recent referendums and initiatives, populists and social movements continue to call for the use of these popular vote processes. Most political and academic debates about whether these calls should be answered have adopted a dominant framework that focuses on whether we should favour ‘direct’ or ‘representative’ democracy. However, this framework obscures more urgent questions about whether, when, and how popular vote processes should be implemented in democratic systems. How do popular vote processes interact with representative institutions? And how could these interactions be democratized? The contributions in this special issue address these and related questions by replacing the framework of ‘direct democracy’ with systemic approaches. The normative contributions illustrate how these approaches enable the development of counternarratives about the value of popular vote processes and clarify the nature of the underlying ideals they should realize. The empirical contributions examine recent cases with a variety of methodological tools, demonstrating that systemic approaches attentive to context can generate new insights about the use of popular vote processes. This introduction puts these contributions into conversation to illustrate how a shift in approach establishes a basis for (re-)evaluating existing practices and guiding reforms so that referendums and initiatives foster democracy.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.028 | 0.009 |
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".