“What Would the Public Think if It Had a Chance to Think?” (Deliberative Forums as a Cure for Democratic Deficit)
Bibliographic record
Abstract
The theory of deliberative democracy is well known and has been studied thoroughly so far; however, the practical side of the functioning of different types of deliberative forums is discussed not so frequently, especially in Russian-language literature. Meanwhile, over the past decades, various countries around the world have accumulated a great deal of experience in the work of consultative mini-publics: one can recall the National Forum in Iceland, convened to discuss the draft of a new Constitution, regular National Public Policy Conferences in Brazil, the Citizens’ Convention on Climate in France, the Citizens’ Assembly on Electoral Reform in Canada, and much more. The article presents an analysis of this experience. Having focused on two most common types of deliberative institutions — citizens’ assemblies and deliberative polls, — the author attempts to find an answer to the question: can deliberative democracy help to solve key problems of representative political system, such as depoliticization of society, decline of trust in political institutions, etc? Drawing on the results of the conducted research, the author comes to the conclusion that deliberative practices can really contribute to the revival of liberal democracy and repoliticization of society by supplementing customary institutions of political participation with new forms of citizens’ involvement in politics, and many experiments of that kind look truly promising. At the same time, according to the author, deliberative democracy does not have sufficient transformational capabilities to drastically change the political status quo.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.051 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".