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Short Abstract

Using crowdsourcing theory, we reframe citizen science as open, distributed problem solving. Profiling contributions (activities, knowledge, resources, decisions) and tuning design levers (tasks, allocation, rewards, information) can boost performance and inclusion - with AI as a partner.

Abstract

Crowdsourcing theories offer a powerful lens to understand and improve citizen science. Rather than treating “citizen” projects as sui generis, we frame them as a form of open, distributed problem solving where tasks, knowledge, resources, and decision rights are modularized and recombined across actors using different "crowd paradigms". Using this lens clarifies two things. First, the often-separate traditions of “Citizen Science” and “Crowd Science” study largely the same phenomenon but privilege different outcomes.

Second, a crowdsourcing perspective enables a crisp, multi-dimensional profiling of projects along four contribution types—activities, knowledge, resources, and decisions—revealing who does what, with which inputs, and who decides. This profile travels well across domains (eBird, Zooniverse, Foldit, Polymath) and makes visible where citizen contributions are essential.

Viewing citizen science as a “new form of organizing” yields a tractable agenda on four design levers: task division (granularity, timing), task allocation (self-selection vs. assignment), provision of rewards (beyond authorship to intrinsic and local value), and provision of information (training, feedback, coordination). These levers link directly to performance and inclusiveness and are amenable to rigorous testing on platforms that log processes and outcomes.

Finally, the crowdsourcing lens helps position AI not only as infrastructure but as a “third actor.” Hybrid human–machine systems now route tasks dynamically and shift volunteer effort from repetitive labeling to higher-order discovery, with implications for equity, engagement, and impact. The upshot: applying crowdsourcing theory provides a common language and actionable design principles to unlock both the productivity and democratization potential of citizen science.