Deliberating in an Emergency: Participant Experiences and Public Perceptions of Deliberative Mini-Publics on Climate Change in Canada and the United Kingdom
Bibliographic record
Abstract
This article-based dissertation project includes three distinct empirical studies which consider different aspects of deliberative mini-publics (DMPs). A deliberative mini-public is a process in which randomly-selected citizens are gathered to learn about, deliberate on and provide recommendations dealing with a specific policy issue. These processes have become increasingly popular as a tool for policy-makers who are facing complex or controversial policy decisions, and many recent cases have considered the issue of climate change. \n \nThe introductory paper of this dissertation provides a brief history of the use of DMPs and introduces current debates in the study of DMPs, particularly in terms of their implications for political behaviour and public opinion. The first study compares the political efficacy and behaviour of DMP members before and one year after participating in a DMP (Climate Assembly UK). This study shows that members reported higher internal political efficacy, higher political capacity scores, and increases in the rates of their political participation after Climate Assembly UK. The second study asks why members chose to participate in a DMP. Drawing on interview data with participants, I find that both interest in the topic of deliberation and the design of the deliberative experience is crucial to a member’s decision to participate in a DMP. The third study outlines how DMPs have been used differently in the Canada and UK context and examines how the public perceives DMPs in these two country countries. I find no differences in public perceptions attributable to country context and a strong preference for more traditional methods of citizen engagement over the DMP model. \n \nThis project provides insights into the broader political implications of this relatively novel method of citizen participation. I contribute to debates about the capacity of citizen engagement to address the issues of democratic malaise and disengagement from traditional avenues of citizen participation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".