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1. Place-based and data-rich citizen science as a precursor for conservation action: Citizen Science and Conservation Action

   https://doi.org/10.1111/cobi.12702  

   Haywood, Benjamin K. ; Parrish, Julia K. ; Dolliver, Jane ( June 2016 , Conservation Biology)
2. Inclusion in citizen science: The conundrum of rebranding Does replacing the term “citizen science” do more harm than good?

   Cooper, Caren ; Hawn, Chris ; Larson, Lincoln ; Parrish, Julia ; Bowser, Gillian ; Cavalier, Darlene ; Dunn, Robert ; Haklay, Mordechai ; Gupta, Kaberi Kar ; Jelks, Na'Taki Osborne ; et al ( June 2021 , Science)

   As the scientific community, like society more broadly, reckons with long-standing challenges around accessibility, justice, equity, diversity, and inclusion, we would be wise to pay attention to issues and lessons emerging in debates around citizen science. When practitioners first placed the modifier “citizen” on science, they intended to signify an inclusive variant within the scientific enterprise that enables those without formal scientific credentials to engage in authoritative knowledge production (1). Given that participants are overwhelmingly white adults, above median income, with a college degree (2, 3), it is clear that citizen science is typically not truly an egalitarian variant of science, open and available to all members of society, particularly those underrepresented in the scientific enterprise. Some question whether the term “citizen” itself is a barrier to inclusion, with many organizations rebranding their programs as “community science.” But this co-opts a term that has long referred to distinct, grassroots practices of those underserved by science and is thus not synonymous with citizen science. Swapping the terms is not a benign action. Our goal is not to defend the term citizen science, nor provide a singular name for the field. Rather, we aim to explore what the field, and the multiple publics it serves, might gain or lose by replacing the term citizen science and the potential repercussions of adopting alternative terminology (including whether a simple name change alone would do much to improve inclusion). 

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3. Facilitator organizations enhance learning and action through citizen science: a case study of Girl Scouts’ Think Like a Citizen Scientist journey on SciStarter

   https://doi.org/10.1080/13504622.2023.2237705  

   Smith, Haley E. ; Cooper, Caren B. ; Busch, K.C. ; Harper, Suzanne ; Muslim, Amy ; McKenna, Kaleigh ; Cavalier, Darlene ( July 2023 , Environmental Education Research)
4. Citizen ASAS-SN Data Release. I. Variable Star Classification Using Citizen Science

   https://doi.org/10.1088/1538-3873/ac44f0  

   Christy, C. T. ; Jayasinghe, T. ; Stanek, K. Z. ; Kochanek, C. S. ; Way, Z. ; Prieto, J. L. ; Shappee, B. J. ; Holoien, T. W.-S. ; Thompson, T. A. ; Schneider, A. ( February 2022 , Publications of the Astronomical Society of the Pacific)

   Abstract We present the first results from Citizen ASAS-SN, a citizen science project for the All-Sky Automated Survey for Supernovae (ASAS-SN) hosted on the Zooniverse platform. Citizen ASAS-SN utilizes the newer, deeper, higher cadence ASAS-SN g -band data and tasks volunteers to classify periodic variable star candidates based on their phased light curves. We started from 40,640 new variable candidates from an input list of ∼7.4 million stars with δ < −60° and the volunteers identified 10,420 new discoveries which they classified as 4234 pulsating variables, 3132 rotational variables, 2923 eclipsing binaries, and 131 variables flagged as Unknown. They classified known variable stars with an accuracy of 89% for pulsating variables, 81% for eclipsing binaries, and 49% for rotational variables. We examine user performance, agreement between users, and compare the citizen science classifications with our machine learning classifier updated for the g -band light curves. In general, user activity correlates with higher classification accuracy and higher user agreement. We used the user’s “Junk” classifications to develop an effective machine learning classifier to separate real from false variables, and there is a clear path for using this “Junk” training set to significantly improve our primary machine learning classifier. We also illustrate the value of Citizen ASAS-SN for identifying unusual variables with several examples. 

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5. Citizen ASAS-SN: Citizen Science with The All-Sky Automated Survey for SuperNovae (ASAS-SN)

   https://doi.org/10.3847/2515-5172/abe8de  

   Christy, C. T. ; Jayasinghe, T. ; Stanek, K. Z. ; Kochanek, C. S. ; Way, Z. ; Prieto, J. L. ; Shappee, B. J. ; Holoien, T. W.-S. ; Thompson, T. A. ( February 2021 , Research Notes of the AAS)

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