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This paper explores the assumptions that citizen science (CS) project leaders had about their volunteers’ science inquiry skill–proficiency overall, and then examines volunteers’ actual proficiency in one specific skill, scientific observation, because it is fundamental to and shared by many projects. This work shares findings from interviews with 10 project leaders related to two common assumptions leaders have about their volunteers’ skill proficiency: one, that volunteers can perform the necessary skills to participate at the start of a CS project, and therefore may not need training; and two, volunteer skill proficiency improves over time through involvement in the CS project. In order to answer questions about the degree of accuracy to which volunteers can perform the necessary skills and about differences in their skill proficiency based on experience and data collection procedures, we analyzed data from seven CS projects that used two shared embedded assessment tools, each focused on skills within the context of scientific observation in natural settings: Notice relevant features for taxonomic identification and record standard observations. This across-project and cross-sectional study found that the majority of citizen science volunteers (n = 176) had the necessary skill proficiency to collect accurate scientific observations but proficiency varied based on volunteer experience and project data collection procedures.
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Reimagining Meaningful Data Work through Citizen Science
Data work is often completed by crowdworkers, who are routinely dehumanized, disempowered, and sidelined. We turn to citizen science to reimagine data work, highlighting collaborative relationships between citizen science project managers and volunteers. Though citizen science and traditional crowd work entail similar forms of data work, such as classifying or transcribing large data sets, citizen science relies on volunteer contributions rather than paid data work. We detail the work citizen science project managers did to shape volunteer experiences: aligning science goals, minimizing barriers to participation, engaging communities, communicating with volunteers, providing training and education, rewarding contributions, and reflecting on volunteer work. These management strategies created opportunities for meaningful work by cultivating intrinsic motivation and fostering collaborative work relationships but ultimately limited participation to specific data-related tasks. We recommend management tactics and task design strategies for creating meaningful work for invisible collar workers, an understudied class of labor in CSCW.
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- Award ID(s):
- 2310592
- PAR ID:
- 10614836
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Human-Computer Interaction
- Volume:
- 8
- Issue:
- CSCW2
- ISSN:
- 2573-0142
- Page Range / eLocation ID:
- 1 to 26
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3687049
