Abstract
At smaller social scales, deliberative democratic theory can be restated as an input-process-output model. We advance such a model to formulate hypotheses about how the context and design of a civic engagement process shape the deliberation that takes place therein, as well as the impact of the deliberation on participants and subsequent policymaking. To test those claims, we extract and code case studies from Participedia.net, a research platform that has adopted a self-directed crowd-sourcing strategy to collect data on participatory institutions and deliberative interventions around the world. We explain and confront the challenges faced in coding and analyzing the Participedia cases, which involves managing reliability issues and missing data. In spite of those difficulties, regression analysis of the coded cases shows compelling results, which provide considerable support for our general theoretical model. We conclude with reflections on the implications of our findings for deliberative theory, the design of democratic innovations, and the utility of Participedia as a data archive.
Highlights
The results show that there are interesting patterns of associations that emerge from the Participedia data
The principal cause of reduced sample size, was neither listwise deletion nor outlier removal, but rather the difficulty of coding many cases owing to insufficiently detailed descriptions in the relevant Participedia case. We revisit this challenge in our concluding section, when we suggest the implications of this study for Participedia and similar data repositories
This article is a response to a weakness in the literature on democratic innovations
Summary
Our aim was to explore associations among key features of participatory processes. We aimed to generate variability in the deliberative characteristics of cases in order that we could study the relationships between such variations within our input-process-output model.
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