The Potential of <i>Participedia</i> as a Crowdsourcing Tool for Comparative Analysis of Democratic Innovations
Bibliographic record
Abstract
Participedia (PP; www.participedia.net ) is an open global knowledge platform for researchers and practitioners in the field of democratic innovation and public engagement. It represents an experiment with a new and potentially powerful way to conduct social science research: crowdsourcing data on participatory processes from researchers and practitioners from all over the world and making that data freely available for analysis. This article reflects on the potential of PP to realize its long‐term aim of answering the basic research questions: what kinds of participatory processes work best, for what purposes, and under what conditions? Initially the article reviews the data model that informs PP and the types of comparative analysis it might enable. Our analysis draws on the PP data to explore the relationship between aspects of institutional design (including facilitation, forms of interaction, and decision methods) across a range of democratic innovations represented on the platform. The study offers important insights on institutional design, but also on the potential for crowdsourcing data from disparate communities.
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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 | 0.133 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".