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  1. Makeda Cheatom
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  1. Karen Purcell
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  1. Bobby Wilson
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  4. Metro Atlanta Urban Farm
  1. Tanya Zwald
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  5. Comunidades Latinas Unidas en Servicio (CLUES)

Supporting Equitable Approaches for Co-created Research from the Ground Up

NSF Awards: 1811234

2019 (see original presentation & discussion)

Undergraduate, Graduate, Adult learners, Informal / multi-age

Historically, the majority of programming, evaluation and research on citizen science has been led by academic institutions, and frameworks and approaches, even when following culturally responsive techniques, have been informed by dominant culture worldviews. In view of the growing mistrust of science institutions, and the power inequities between these institutions and the community organizations needed to support projects, these approaches and motivations rarely work. We share a different model, led by 15 representatives from underserved communities. Our community-led approach aims to improve equity, diversity, and inclusion in Public Participation in Scientific Research (PPSR) as we document and self-reflect on the equitable co-creation of a community science project.  

With funding from NSF, we have co-created a framework to increase meaningful engagement of diverse audiences in science. Over the next three years we will ground-truth our framework as we co-create a model community science project, for use at the continental scale. The project will connect underserved communities with science in authentic ways and produce research results and educational materials that directly benefit the participating communities. The project will bring awareness about noise pollution, an issue of concern identified by the communities, and promote wellbeing and connection with nature. We intend to generate data to potentially change policy in neighborhoods where noise may be interfering with human health. Our co-created research will include methodologies that have the power to give voice to perspectives that have historically been silenced through traditional research and can be used to co-create more appropriate and relevant research metrics.

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