A collaborative framework to improve public participation practice

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Abstract

In Indonesia, an annual participatory planning practice called Musrenbang is implemented to produce village development plans through public meetings. Being an obligatory process, Musrenbang is often poorly implemented due to problems such as power relations and disagreement among stakeholders. Enabling the stakeholders to find common understanding through knowledge integration is crucial to minimize these issues. As most villages do not have proper maps, we developed a collaborative spatial learning methodology to enable the village stakeholders to participate in the mapping process. Through the mapping exercise it is expected that we can support production of the village maps, and contribute to integrate stakeholders’ spatial knowledge; helping them to minimize the power gaps and to find common understanding through social learning experiences. Ultimately, it is expected that the developed methodology will improve the Musrenbang implementation at village level.
Original languageEnglish
Title of host publicationProceedings of the 16th Participatory Design Conference. Volume II
Subtitle of host publicationExploratory Papers and Workshops
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages133-137
Number of pages5
Volume2
ISBN (Electronic)978-1-4503-7606-8/20/06.
DOIs
Publication statusPublished - 15 Jun 2020

Publication series

NameACM International Conference Proceeding Series
PublisherAssociation for Computing Machinery

Keywords

  • Musrenbang
  • participatory mapping
  • participatory planning
  • public participation
  • spatial knowledge
  • Knowledge co-production
  • Social learning

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    Akbar, A., Flacke, J., Martinez, J., & van Maarseveen, M. F. A. M. (2020). A collaborative framework to improve public participation practice. In Proceedings of the 16th Participatory Design Conference. Volume II: Exploratory Papers and Workshops (Vol. 2, pp. 133-137). (ACM International Conference Proceeding Series). New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/3384772.3385126