The Diversity of Mini-Publics: A Systematic Overview
Bibliographic record
Abstract
Introduction The burgeoning literature on DMPs has studied and debated the merits of this form of democratic innovation. It is striking that this field of research contains no unanimously accepted definition of DMPs. As explained in Chapter One of this book, our goal is not to determine which definition is the most appropriate. Rather, we work with a definition of DMPs based upon two basic constitutive elements: (1) it should be a mini-public, meaning participants are selected through a process that generates a representative sample of the public; and (2) it should be a deliberative process, meaning that participating citizens reach their conclusions or recommendations after receiving information and engaging in a careful and open discussion about the issue or issues before them. We build from this to examine the diversity of real-life examples of DMPs that have taken place over the last two decades. Real-world DMPs are indeed diverse, ranging from planning cells to citizens’ assemblies, consensus conferences and deliberative polls. This chapter derives from the empirical diversity of DMPs a general description of their organization and core design features, and the ways in which they have been implemented across countries. In particular, we will build upon the inventory of DMPs instituted by national and regional public authorities across Europe produced within the POLITICIZE project. This data set, which has been gathered by one of the authors of this book, has identified and described over 120 different cases since 2000. We have chosen this data set because it provides a comprehensive inventory of mini-publics. We recognize that this data set only covers European cases and that there are other data sets with broader coverage, such as the one compiled by the OECD or the Doing Mini-publics project. Nonetheless, we find this data set valuable, for it provides detailed information regarding how the mini-publics were composed and organized, as well as on the topics deliberated and on the outcomes. To enrich our analysis, we also bring in insights from other DMPs that have occurred outside Europe or before 2000 that are not covered by this inventory. Capitalizing on this original data set, the chapter describes the core features of DMPs along three dimensions: their composition; their format and topic of deliberation; and their outputs.
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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 | 0.031 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.040 | 0.051 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".