Valuing Environmental Public Goods: Deliberative Citizen Juries as a Non-Rational Persuasion Method
Bibliographic record
Abstract
Governments sometimes use committees of selected volunteers to provide comment on environmental policy choices. We use a repeated choice experiment to explore how a deliberative citizen jury (DCJ) treatment affects the conservation preferences of DCJ participants who engage in a budget allocation exercise. First round choice experiment participants were invited to volunteer for one of a pair of paid DCJ sessions. Stated preference results for the DCJ participants were compared with a pseudo-control formed by matching non-participants on socioeconomic characteristics. Both preference and response heterogeneity declines for the DCJ treatment group, relative to the control. The stated preference results for the DCJ group are significantly different from those for the total sample, and the DCJ budget allocation results are inconsistent with the preferences expressed by the total sample. DCJ style committees may reflect how educated citizens make choices. However, selection and impacts of the deliberation make it likely these committees are not representative of the broader population.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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 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".