Mini-Public Replication: Emotions and Deliberation in the Citizens' Initiative Review Redux
Bibliographic record
Abstract
Scholars have increasingly urged researchers to evaluate prior findings through replication studies that can help test, refine, and extend claims made in previous research. We agree that this is an important aspect of social science that deliberative scholarship has underutilized. To help fill this lacunae, we test our previous findings from an analysis of data from Citizen Initiative Reviews (CIRs) in 2016 by replicating our methodology on data from CIRs in 2018. We set out to determine if the patterns we discovered earlier and developed into the Deliberative Procedures Frame theory appeared again in 2018 CIRs. We find repeating across the two sets of data, including consistent levels of enthusiasm, slow rising happiness, and the relationships between certain emotions on the final day and participants’ evaluations of deliberative quality, and these indicate that our theory remains a viable explanation for emotions in mini-public deliberation. We remain confident that the sources of anger and frustration identified in our previous analysis remains correct. On the basis of this replication, we clarify that what we call the Procedures Frame enables the identification of the most likely time points during deliberation when the threat to democratic legitimacy and the risk to quality deliberation will most likely arise and result in expressions of emotion. Finally, our study reinforces how important deliberative design is to the role of emotions in the success of mini-publics.
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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 | 0.127 | 0.407 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".