Redistrict: Online Public Deliberation Support that Connects and Rebuilds Inclusive Communities

Author:

Sistrunk Andreea1ORCID,Self Nathan2ORCID,Biswas Subhodip3ORCID,Luther Kurt2ORCID,Verdezoto Nervo4ORCID,Ramakrishnan Naren5ORCID

Affiliation:

1. Virginia Tech, Arlington, VA, USA

2. Dept. of Computer Science, Virginia Tech, Arlington, VA, USA

3. Computer Science, Virginia Tech, Arlington, VA, USA

4. School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom

5. Computer Science, Virginia Tech, Blacksburg, VA, USA

Abstract

Public deliberations are often a staple ingredient in community decision-making. However, traditional, time-constrained, in-person debates can become highly polarized, eroding trust in authorities, and leaving the community divided. This is the case in redistricting deliberations for public school zoning. Seeking alternative ways of support, we evaluated the potential introduction of an online platform that combines multiple streams of data, visualizes school attendance boundaries, and enables the manipulation of representations of land parcels. To capture multiple stakeholders' values about the potential to enhance public engagement in school rezoning decision-making through an online platform, we conducted interviews with 12 participants with previous experiences in traditional, in-person deliberations. Insights from the interviews highlight the several roles an online platform could take, especially as it provides alternative means of participation (online, synchronous, and asynchronous). Additionally, we discuss the potential for technology to increase the visibility and participation of multiple community actors in public deliberations and present implications for the design of future tools to support public decision-making.

Funder

NSF

Publisher

Association for Computing Machinery (ACM)

Reference90 articles.

1. AACP. 0. U.S. Census Bureau Demographics. https://www.census.gov/.

2. AL. 2014. Public Rezoning Meeting. Over the mountain journal.

3. Paul André, Aniket Kittur, and Steven P. Dow. 2014. Crowd Synthesis: Extracting Categories and Clusters from Complex Data. In Proceedings of CSCW 2014. unknown, unknown, unknown.

4. Civic Technologies

5. Creating a Sociotechnical API

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