The Scope of Climate Assemblies: Lessons from the Climate Assembly UK
Bibliographic record
Abstract
In recent times we have seen a spate of climate assemblies across Europe as the climate emergency gains increasing prominence in the political agenda and as the citizens’ assembly approach to public engagement gains popularity. However, there has been little empirical research on how the scope of citizens’ assemblies affects the internal logic of the assembly process and its impacts on external policy actors. This is a significant oversight given the power of agenda setting. It is also of particular importance for climate assemblies given the exceptional scale and complexity of climate change, as well as the need for co-ordination across all policy areas and types of governance to address it. In this paper, we start to address this gap through an in-depth case analysis of the Climate Assembly UK. We adopt a mixed methods approach, combining surveys of the assembly members and witnesses, interviews with the assembly members, organisers, MPs, parliamentary staff, and government civil servants, and non-participant observation of the process. We find that attempts to adapt the assembly’s scope to the scale of the climate change issue compromised assembly member learning, the co-ordination of the resulting recommendations, assembly member endorsement of the recommendations, and the extent of their impact on parliament and government. We argue that more democratization in setting the agenda could help combat these issues.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".