Democratic Deliberation in the Wild: The McGill Online Design Studio and the RegulationRoom Project
Bibliographic record
Abstract
Although there is no single unified conception of deliberative democracy, the generally accepted core thesis is that democratic legitimacy comes from authentic deliberation on the part of those affected by a collective decision. This deliberation must occur under conditions of equality, broadmindedness, reasonableness, and inclusion. In exercises such as National Issue forums, citizen juries, and consensus conferences, deliberative practitioners have shown that careful attention to process design can enable ordinary citizens to engage in meaningful deliberation about difficult public policy issues. Typically, however, these are closed exercises-that is, they involve a limited number of participants, often selected to achieve a representative sample, who agree to take part in an extended, often multi-stage process. The question we begin to address here is whether the aspirations of democratic deliberation have any relevance to conventional public comment processes. These processes typically allow participation that is universal (anyone who shows up can participate) and highly variable (ranging from brief engagement and short expressions of outcome preferences to protracted attention and lengthy brief-like presentations). Although these characteristics preclude the kind of control over process and participants that can be achieved in a deliberation exercise, we argue that conscious attention to process design can make it more likely that more participants will engage in informed, thoughtful, civil, and inclusive discussion. We examine this question through the lens of two action-based research projects: the McGill Online Design Studio (MODS), which facilitates public participation in Canadian urban planning, and RegulationRoom, which supports public comment in U.S. federal rulemaking.
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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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".