From Conservation to Crowdsourcing: A Typology of Citizen Science

By Andrea Wiggins, Kevin Crowston. Hawaii International Conference on System Sciences, 2011

684 citations recorded by the source corpus.

Citizen science is a form of research collaboration involving members of the public in scientific research projects to address real-world problems. Often organized as a virtual collaboration, these projects are a type of open movement, with collective goals addressed through open participation in research tasks. Existing typologies of citizen science projects focus primarily on the structure of participation, paying little attention to the organizational and macrostructural properties that are important to designing and managing effective projects and technologies. By examining a variety of project characteristics, we identified five types-Action, Conservation, Investigation, Virtual, and Education- that differ in primary project goals and the importance of physical environment to participation.

Subfields: Communication, Computer Science Applications, Management of Technology and Innovation

View Paper: https://www.semanticscholar.org/paper/83bd9eb9b59c1c38b49e79a33d7285fe4df48777

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Citations in the corpus

Statistical solutions for error and bias in global citizen science datasets (2014, background): “…of how the data type they collect will influence the kinds of questions 190 they can ask, what statistical tools will be available to do so, and what 191 additional information should be collected with each response data point for 192 analyses (Wiggins et al., 2011, Wiggins and Crowston, 2011).”
Amazon Mechanical Turk: A Research Tool for Organizations and Information Systems Scholars (2012, neutral): “In citizen science projects, members of the public are recruited to contribute to scientific investigations [23, 24].”
Evaluation on crowdsourcing research: Current status and future direction (2012, background): “…that the crowd can provide valuable results and can actually compete with professionals in some cases (Hsueh et al. 2009; Poetz and Schreier 2009; Wiggins & Crowston 2011), many people still doubt the quality of feedbacks, especially related to science or business innovation issues that may need…”
- in-Progress (2012, neutral): “” Organizations are currently leveraging the crowd to improve algorithms (Lindley, 2009), to create new knowledge bases (Ghafele et. al., 2011; Wiggins and Crowston, 2011), to collect distributed data (Chilton, 2009; Haklay and Weber, 2008), to solve routine time-consuming tasks (Barrington et.”
ORES (2019, background): “s adjacent issues as they play out in citizen science and crowdsourcing, which is often used as a way to collect, process, and verify training data sets at scale, to be used for a variety of purposes [52,60,107]. 2.2 Machine learning in support of open production Open peer production systems, like m”
Analytical guidelines to increase the value of community science data: An example using eBird data to estimate species distributions (2021, background): “The number of these CS projects has been growing exponentially, but they vary widely in complexity, data collection flexibility and participation (Pocock et al., 2017; Wiggins & Crowston, 2011).”
Crowdsourcing for climate and atmospheric sciences: current status and future potential (2015, neutral): “Citizen Science 17 18 Citizen science is a form of collaborative research involving members of the public: volunteers, 19 amateurs and enthusiasts (Goodchild, 2007; Wiggins and Crowston, 2011; Roy et al., 2012).”
Dynamic changes in motivation in collaborative citizen-science projects (2012, background): “For all these reasons, and although the contribution of volunteers to the development of science has been well documented [11-14], their participation is often considered peripheral.”