When facilitation is not enough: the role of games in small-group discussions
Bibliographic record
Abstract
Abstract While scholars of deliberative democracy have long conceded that good deliberation requires careful facilitation, little attention has been paid to the effects of different facilitation methods. This paper has three aims. First, it establishes the importance of facilitation. Second, it argues that facilitation may not be enough to counteract the imbalances in power and influence within deliberation. As such, this paper introduces two games that can be utilized in concert with facilitation: deliberative worth exercises and simulated representation. The former pushes participants to remain aware of their behavior patterns within deliberation by asking them to choose the best deliberator at the end of each round of deliberation; the latter enables empathy and perspective-taking by partnering participants and asking them to represent one another’s viewpoints for a portion of deliberation as if they were their own. Third, using proof-of-concept experiments, this paper demonstrates the efficacy of these games.
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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 | 0.005 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".