The Potential of <i>Participedia</i> as a Crowdsourcing Tool for Comparative Analysis of Democratic Innovations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it