Bibliographic record
Abstract
This study aims to build on Moore and O'Doherty's (2014) proposal to integrate deliberative voting procedures into deliberative processes. Deliberative voting has been proposed to recognize collective endpoints of deliberation and solicit key reasons from participants for supporting (or rejecting) collective decisions. This article further develops the theoretical understanding of the function of embedding voting procedures in deliberative processes. Using discursive psychological analysis, we provide an analysis of transcripts from a public deliberation event on cancer drug funding policy to gain a deeper understanding of the discursive dynamics of deliberative voting. We investigate how participants use deliberative voting as a communication tool to signal three types of disagreement: actual, nuanced, and marginal. We pay particular attention to the role of the facilitator in the deliberative voting process and the role of the voting process in shaping the outputs of the deliberation. Finally, we recommend deliberation practitioners and facilitators should engage in reflexive investigation into how power operates within deliberative voting and deliberation events broadly.
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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 | 0.052 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".