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Mass Online Deliberation in Participatory Policy-Making—Part I

Rationale, Lessons from Past Experiments, and Requirements
  • Cyril VelikanovEmail author
  • Alexander Prosser
Chapter
Part of the Public Administration and Information Technology book series (PAIT, volume 25)

Abstract

This two-part chapter proposes a model and some design choices to build a Mass Online Deliberation (MOD) system, aimed at supporting orderly, fair, inclusive and purposeful participation of a large number of people. According to this model, a deliberation on a given issue, in a given community and at a given time (a “deliberandum”), progresses through a number of phases, roughly corresponding to ideation (moving and discussing proposals, with a proposals’ clustering algorithm operating in the background), consolidation (i.e. editing of one proposal per cluster) and reconciliation (of some among the consolidated proposals from different clusters). Depending on a given context of use, a final selection of one among the remaining irreconcilable proposals may be done by vote either among the deliberants only, or within the whole community (a referendum), or else, within a randomly selected panel of community members. The specific mechanisms defined in our model are as follows: mutual moderation and two- or three-parametric appraisal of each other’s contributions (hence without employing any staff of external moderators or facilitators); semantic clustering of a large number of proposals, performed in the background by the system and mostly based on the distribution of participants’ appraisals among contributions; and also some specific role of experts in the field, whose participation is limited to providing facts and replying to factual questions, not to actively influence participants’ opinions. Multilingual mass deliberation is discussed at the end of the Part II of the chapter.

Keywords

Social Medium Public Participation Trained Moderator Natural Language Processing Tool National Forum 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.International Memorial SocietyMoscowRussia
  2. 2.Department of Informations Systems and OperationsWU Wien, Vienna University of Economics and BusinessViennaAustria

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