Emotions and Deliberation in the Citizens’ Initiative Review
Bibliographic record
Abstract
Objective Emotions in deliberative democratic practices have been of interest to researchers and practitioners of democracy for years. Yet, scholars have not fully analyzed emotions in this context. We advance this discussion in terms of both data collection and analysis with respect to Citizens' Initiative Reviews (CIRs) in Arizona, Oregon, and Massachusetts in 2016. We respond to four central research questions: (1) What discrete emotions do participants report experiencing during mini‐public deliberation? (2) How do the reported emotions vary across the period of deliberation? (3) How do the expressed emotions affect the deliberation? and (4) What work do expressed emotions do in mini‐publics in terms of helping or hindering deliberation? Methods To ensure a comprehensive analysis of the data we were able to collect, we employ a mixed‐methods design and use both quantitative and qualitative methods. Results and Conclusion Ultimately, we contend that the activities and tasks of the group, as well as the behaviors of participants and relationships among them, are all important factors that shape how people experience emotion, but that the CIR procedures have the greatest influence in mediating emotions to serve the ends of deliberation in these 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 | 0.032 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".