Abstract

Citizen science (CS) has been an increasingly utilized means by which scientists leverage members of the public to increase the amount of data collected and analyzed. However, the underrepresentation of individuals from certain socio-cultural groups can have consequences that can manifest in the scientific outcomes of those CS projects such as biases in the data. Additionally, this underrepresentation can potentially affect long-term viability and support of CS as a community of practice. CS programs that promote greater inclusivity would likely provide opportunities for communities to define, investigate, and address pressing issues in collaboration with professional scientists. In this paper we discuss a CS project that sought to include underrepresented communities in Baltimore, Maryland using Pandya’s framework for inclusive CS. While the project met all of its scientific research goals, translating the CS for broader social outcomes in the community proved challenging. Here we highlight perspectives from local community members and research personnel about the barriers to CS engagement, challenges in translating scientific outcomes to social justice efforts, and opportunities to address these barriers in CS program development and design.

Highlights

  • The number of scientific research projects that use citizen science in some capacity has increased significantly over the past few decades (Conrad and Hilchey 2011; Follett and Strezov 2015)

  • While there has been no formal meta-analysis of representation in citizen science, a recent report on diversity in Citizen science (CS) has found that participants tend to include white and more well-educated individuals (NASEM 2018)

  • We explore issues of representation in and community benefit from CS through the lens of a recent CS project, the Baltimore Mosquito Study

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Summary

CASE STUDIES

Reflecting on Efforts to Design an Inclusive Citizen Science Project in West Baltimore. The underrepresentation of individuals from certain socio-cultural groups can have consequences that can manifest in the scientific outcomes of those CS projects such as biases in the data. This underrepresentation can potentially affect long-term viability and support of CS as a community of practice. We highlight perspectives from local community members and research personnel about the barriers to CS engagement, challenges in translating scientific outcomes to social justice efforts, and opportunities to address these barriers in CS program development and design

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