@article {2024,
	title = {Artificial Intelligence and the Future of Citizen Science},
	journal = {Citizen Science: Theory and Practice},
	volume = {9},
	year = {2024},
	month = {11/2024},
	pages = {32},
	doi = {10.5334/cstp.812},
	author = {Fortson, Lucy and Crowston, Kevin and Kloetzer, Laure and Ponti, Marisa}
}
@article {2021,
	title = {Imagine All the People: Citizen Science, Artificial Intelligence, and Computational Research},
	year = {2021},
	institution = {Computing Community Consortium (CCC)},
	address = {Washington, DC},
	abstract = {<p>Machine learning, artificial intelligence, and deep learning have advanced significantly over the past decade. Nonetheless, humans possess unique abilities such as creativity, intuition, context and abstraction, analytic problem solving, and detecting unusual events. To successfully tackle pressing scientific and societal challenges, we need the complementary capabilities of both humans and machines. The Federal Government could accelerate its priorities on multiple fronts through judicious integration of citizen science and crowdsourcing with artificial intelligence (AI), Internet of Things (IoT), and cloud strategies.</p>},
	url = {https://cra.org/ccc/wp-content/uploads/sites/2/2021/03/CCC-TransitionPaperImagine-All-the-People.pdf},
	author = {Lea A. Shanley and Lucy Fortson and Tanya Berger-Wolf and Kevin Crowston and Pietro Michelucci}
}
@article {9999,
	title = {Stages of motivation for contributing user-generated content:  A theory and empirical test},
	journal = {International Journal of Human-Computer Studies},
	volume = {109},
	year = {2018},
	pages = {89-101},
	publisher = {Syracuse University},
	address = {Syracuse, NY},
	abstract = {<p>User-generated content (UGC) projects involve large numbers of mostly unpaid contributors collaborating to create content. Motivation for such contributions has been an active area of research. In prior research, motivation for contribution to UGC has been considered a single, static and individual phenomenon. In this paper, we argue that it is instead three separate but interrelated phenomena. Using the theory of helping behaviour as a framework and integrating social movement theory, we propose a stage theory that distinguishes three separate sets (initial, sustained and meta) of motivations for participation in UGC. We test this theory using a data set from a Wikimedia Editor Survey (Wikimedia Foundation, 2011). The results suggest several opportunities for further refinement of the theory but provide support for the main hypothesis, that different stages of contribution have distinct motives. The theory has implications for both researchers and practitioners who manage UGC projects.</p>},
	doi = {10.1016/j.ijhcs.2017.08.005 },
	attachments = {https://citsci.syr.edu/sites/crowston.syr.edu/files/crowston\%20fagnot\%20to\%20distribute.pdf},
	author = {Kevin Crowston and Fagnot, Isabelle}
}
@proceedings {crowston2012,
	title = {Amazon Mechanical Turk: A research tool for organizations and information systems scholars},
	volume = {389},
	year = {2012},
	month = {12/2012},
	pages = {210-221},
	publisher = {Springer},
	address = {Tampa, FL},
	isbn = {978-3-642-35141-9},
	issn = {1868-4238},
	doi = {10.1007/978-3-642-35141-9},
	attachments = {https://citsci.syr.edu/sites/crowston.syr.edu/files/3890210.pdf},
	author = {Kevin Crowston},
	editor = {Anol Bhattacherjee and Brian Fitzgerald}
}
