Economics perspectives on science focus on the organization of the production of scientific knowledge (ref 1). Such social production is puzzling for economists because scientific knowledge is a public good – i.e. it is decisively non-rival and quite non-excludable – and hence cannot be produced on the basis of market-based rewards.
Relying on the pioneering contribution of Merton (ref 2), economists Dasgupta and David have provided a criterion of demarcation between basic science and technological innovation based on the different set of rewards that are in place to incentive such production: respectively, priority (publication, eponymy, fame, membership in elite societies, etc.) and property-based market signals (patent, IP, etc.). They have also intriguingly described how the same scientist may move from one system of production to the other, even in the same lab and indeed in the same day (ref 3). While these authors tend to consider such picture as a model of how societies do and indeed should organize the production of science and technology (e.g. they claim theirs is a functionalist rational reconstruction of science), their model may be a local snapshot of science, whose accuracy depends on evolving norms, regulations and even the content of scientific practices. Nevertheless, such reconstruction can be used as reference point to ask questions pertaining how the traditional organization of scientific production changes as a result of the emergence of crowdsourced science, or how the latter compares with the former. An important proviso is of course that “traditional science” is an inaccurate label and that perhaps traditional science in the Merton/Dasgupta/David sense may have never existed exactly in that form.
To my knowledge, there are two analyses of the vast and heterogeneous world of citizen science that set out from such point of departure.
In a study of biohacking communities, Delfanti (Ref 4) has shown that the ethos of these groups is a blend of hacker and Mertonian norm (ie. duty of sharing plus priority of discovery-based incentives). The inclusion of the latter may be thought as a reaction to the erosion of the Mertonian “open science” norms, which have been withering away ever since the early ´80s, when market-based incentives were given full citizenship in the academia (e.g. Bayh-Dole act), a further blurring of the technology/industry vs. science/academia demarcation.
Economist and organization scientists Franzoni and Sauermann (Ref 5), studying a set of crowd science projects ranging from FoldIt to Polymath, argued that while Mertonian science was mainly open with respect to scientific results, citizen science can be described along two entirely new dimensions of “openness”: openness with respect to participation of outsiders, and openness with respect to the disclosure of intermediate inputs (including protocols, data and logs, etc.), dimensions along which Mertonian “traditional science” was instead usually closed.
The second contribution is also important because it highlights the potential benefits of crowdsourced science and describes which tasks are suitable to crowdsourcing, including data collection, coding, collective problem solving and tackling a big number of modular sub-problems. This also suggests that the emergence of citizen science should not be conceived as an alternative to “traditional” science but the increasing visibility and importance of such tasks in scientific projects that rely on massive data and sample sets
2 Merton, R. 1957. Priorities in Scientific Discovery: A Chapter in the Sociology of Science.