Imagining the model citizen: A comparison between public understanding of science,public engagement in science,and citizen science

Abstract

This article examines the visions of citizens’ ideal practices regarding technoscientific affairs in a democratic society, namely “imaginaries of model citizens,” that underlie three science and public initiatives: public understanding of science, public engagement in science, and citizen science. While imaginaries of citizens are performative and necessary to these initiatives, they are often relegated to the background. I argue that such imaginaries are the result of a complex of perceptions on the nature of science, the role of democracy in scientific activities, and the form of “democratizing” science. The imaginary of model citizens in public understanding of science is of literate citizens who should know science sufficiently, use it in daily life, and support science; in public engagement in science, the model citizen is a responsible one who should engage in the governance of technoscientific issues; and in citizen science, a contributive one who should partake in and enjoy creating scientific knowledge.

Keywords

citizen science democracy imaginaries model citizen public engagement in science public understanding of science

1. Introduction

In a commentary in Nature Italy, Calderini and Simone (2021) call on the new Italian government to “give citizens a seat at the table” of scientific research and innovation decision-making, as it is set to release a Recovery and Resilience Plan financed with a loan from the NextGenerationEU initiative that aimed to help the country recover from the COVID-19 crisis. They argue that research and innovation, key in tackling global challenges, should involve citizens in all phases because they are both taxpayers who fund research and the beneficiaries of it. Engaging the public, they further suggest, is an “inclusive and responsible practice” that the government should adopt to foster trust in science and promote equitable national development.

As Calderini and Simone’s (2021) proposal implies, science and technology are integral to both our personal lives and societal politics. The late-twentieth century saw a growing interest in ordinary citizens’ roles in science and technology development, as exemplified by the United Kingdom’s “public understanding of science” (PUS) movement (Yearley, 2005). This evolved into an emphasis on “public engagement in/with science” (PES) that gained traction across many European countries in the early twenty-first century (Miller, 2001; Stilgoe et al., 2014). Almost concurrently and separately, the “citizen science” (CS) initiative began to take shape and proliferate in the United States when a Cornell ornithology lab enlisted the amateur public’s assistance in its research (Bonney, 1996).

In these three science and public initiatives (SPIs), especially in PES and CS, the relationships among science, publics, and democracy are frequently discussed. This has turned democracy/democratization into a “buzzword” (Vincent, 2014) in fields such as science communication, public understanding of science, and science and public policy (e.g. Árnason, 2013; Goven, 2003; Haklay, 2013b; Horst, 2007; Irwin, 2001; Nielsen et al., 2011). In its loosest definition, democracy is “a method of group decision-making characterized by a kind of equality among the participants at an essential stage of the collective decision-making” (Christiano, 2018). However, it is also an “essentially contested concept” (Gallie, 1955), with its use varying in discussions such as “democratizing technology policy” (Goven, 2003: 424), “democratization of geographic knowledge” (Warf and Sui, 2010: 200), “democratic engagement with science and technology” (Irwin, 2014: 73), and “democratize environmental governance” (Kinchy, 2017: 108).

These discussions have explored the rationales, best practices and impacts of specific SPIs, and resulted in significant institutional responses from national governments and the European Union. In fact, Calderini and Simone’s (2021) appeal to the Italian government makes double normative moves: not only does it depict how a responsible, democratic government should act regarding decision-making about scientific research, but it also implicitly imagines how a responsible citizen should behave. The citizens imagined in their appeal, as the counterpart of the imagined “responsible government,” are expected to sit with proper manners when offered a place at “the table.” Although citizens occupy an indispensable position in discussions and practices of SPIs, their presence is typically relegated to the background and seldom sufficiently analyzed.

In this article, I will examine the assumptions about the role of model democratic citizens in the aforementioned SPIs—PUS, PES, and CS. My core questions are as follows: How do these initiatives imagine the proper role of citizens in issues pertaining to science and technology? And how do these roles align with democratic values? To explore these questions, I turn to the notion of imaginaries and will first elucidate how the existing literature on this concept can help structure our investigations. The imaginaries of model citizens thus identified are best understood as ideal types, which are notably prominent within individual SPIs. I will then investigate each initiative by critically analyzing relevant literature such as policy documents and academic publications. The concluding discussion summarizes my findings and explores their broader implications.

2. Between science and democracy: Imaginaries of model citizens

The concept of “imaginary” has been increasingly prevalent in the social sciences beginning in the late-twentieth century (e.g. Anderson, 1991; Appadurai, 1996; Taylor, 2002). The use of the concept typically emphasizes its performativity.¹ Taylor’s (2002) idea of “the social imaginary,” for example, denotes “a sense of the normal expectations that we have of one another, the kind of common understanding which enables us to carry out the collective practices that make up our social life” (p. 106). Imaginaries “at once describe attainable futures and prescribe futures that states believe ought to be obtained” (Jasanoff and Kim, 2009: 120), and are “necessary fictions” that are both causative and performative (Nowotny, 2014: 16). There is also a normative dimension of imaginaries (Taylor, 2002; Welsh and Wynne, 2013). As Smith (2009) argues, imaginaries represent “normatively loaded visions not only of what should be done ‘in the world’ but also how it should be undertaken and why” (p. 462). Imaginaries about science and citizens enable, guide and support SPIs that span both academia and policy-making.

In recent years, the science and technology studies (STS) scholarship has extended the imaginaries concept significantly (McNeil et al., 2016; Sismondo, 2020). One influential move was Jasanoff and Kim’s (2009) coinage of the term “sociotechnical imaginaries,” which refers to “collectively imagined forms of social life and social order reflected in the design and fulfillment of nation-specific scientific and/or technological projects” (p. 120). Initially proposed to explain the disparate nuclear policies of the United States and South Korea, sociotechnical imaginaries involve not only notions of desired social orders such as national development and democracy, but also perceptions of technoscientific artifacts—whether nuclear power is a runaway risk or a vehicle for prosperity. As Jasanoff (2015) contends, such imaginaries are “at once products of and instruments of the coproduction of science, technology, and society in modernity” (p. 19). In a similar vein, when examining imaginaries of how citizens should behave toward science in a democratic society, we must consider how both democracy and science/technology are perceived by researchers and practitioners of the SPIs.

While imaginaries are often used to denote visions about technoscientific objects and related futures, such as smart cities (Sadowski and Bendor, 2019), energy transitions (Rudek, 2022), and digital data (Guay and Birch, 2022), a number of studies have exemplified the concept’s potential to capture how publics are envisaged in various contexts and institutions. For example, Barnett et al. (2012) examine how the nature of “imagined publics” shape designs of public engagement efforts about renewable energy technologies, Stephens et al. (2013) compare the “institutional imaginaries of publics” between the UK and Spanish stem cell banks, Hess (2015) elaborates on the “state imaginary of the public” in the context of public opposition to new technologies, and Marris (2015) explores the construction of “imaginaries of the public” as a threat to synthetic biology. Among others, Welsh and Wynne’s (2013) investigation of the “scientific imaginaries of publics” in the UK policy-making since the 1950s is especially pertinent since the British case is central to the PUS and PES movements. They identify three historical modalities of imaginaries of publics: as passive non-entities, as incipient threats due to scientific deficits, and as politicized threats requiring state control.

Drawing on these strands of scholarship, I introduce “imaginaries of model citizens” to denote visions of citizens’ ideal practices concerning technoscientific matters in a democratic society. Analytically, it consists of two interrelated facets: the scientific part outlines what citizens should know and do about science, while the democratic part addresses how they should behave in accordance with democratic values. A close concept to this term is Irwin’s (2015) “scientific citizenship,” which centers on “an inquiry into the relationship between members of society, especially in their capacity as ‘citizens’, and matters of science, technology, and innovation—or what we can more broadly term ‘sociotechnical futures’” (p. 6). Horst (2007) extends this, proposing that scientific citizenship “can be perceived as a normative ideal concerning the appropriate form of democratic governance in a society that has become increasingly dependent on scientific knowledge” (p. 151). Whereas Irwin’s original conceptualization does not emphasize democracy and Horst’s interpretation restricts the science-society relationship to governance, this article’s notion of imaginaries underscores the performativity of democratic values and suggests a broader array of science-citizen-democracy relationships beyond governance.

This article examines the imaginaries of model citizens as collectively held by advocates and practitioners within the PUS, PES, and CS movements. While these initiatives can co-exist in the discursive space, the blend may vary across societies and evolve over time. My discussions on PUS and PES will primarily focus on the UK and other European contexts, whereas those on CS will draw largely from US cases. Moreover, given the heterogeneity within each SPI,² my analysis will spotlight “origin stories” and prioritize traditional, institutionalized approaches within each initiative, as reflected in policies, reports, funding, publications, and academic groups. Despite their often ambiguous presence, imaginaries of citizens are essential for enabling the SPI dialogues and practices, and are best captured by comparing the three initiatives. Earlier studies of imaginaries have leveraged comparisons between different collectives (e.g. Jasanoff and Kim, 2009; Stephens et al., 2013) or across time periods (e.g. Smith, 2009; Welsh and Wynne, 2013).

My starting point is that all these initiatives have imagined a role for citizens in science that aligns with democratic values. In each case, we will identify and systematically compare the core assumptions about science, citizens, and democracy. I argue that these initiatives embody the democratic principle in various ways, each with distinct perceptions of science’s nature and value, as well as democracy’s role in science, and that it is a delicate complex of these perceptions that has shaped the imaginaries of model citizens in a scientific and democratic society. The following sections will examine these complexes in each SPI and show their differences through a critical review of existing research papers, policy documents, and reports on its theories and practices.

3. PUS: Guarding science through literate citizens

The PUS movement in the UK represents an early awakening to the essential role of ordinary citizens in the growth and impacts of science. The movement’s imaginary of model citizens is manifested in the its content, rationales, stimuli, and approaches, and has been rooted in democratic values. An iconic event on its agenda is the publication of a report titled “The Public Understanding of Science,” also known as the “Bodmer Report,” in 1985 by the Royal Society (Durant et al., 1992; Miller, 2001; Wynne, 1992b). The report listed major reasons to promote the public’s understanding of science and urged various stakeholders, including schools, parliamentary and scientific committees, media, industries, scientists, and the Royal Society, to take action to improve the unsatisfying situation. What did it mean exactly by “public understanding of science”? The “science” here covered natural sciences, technology, engineering, and medicine, while the “understanding” involved both the facts and nature of science—its “scope, the limitations, the findings, and the methods” (Royal Society, 1985: 9). As for the “public,” the report specified five “non-scientific” categories (Royal Society, 1985: 7; italics my own):

Private individuals for their personal satisfaction and well-being;

Individual citizens for participation in civic responsibilities as members of a democratic society;

People employed in skilled and semi-skilled occupations, the large majority of which now have some scientific content;

People employed in the middle ranks of management and in professional and trades union associations;

And people responsible for major decision-making in our society, particularly those in industry and government.

The initiative emphasized science’s overwhelmingly positive role in society: science was a good thing and so was knowing about science. The Bodmer Report claimed that its basic thesis was that “better public understanding of science can be a major element in promoting national prosperity, in raising the quality of public and private decision-making and in enriching the life of the individual” (Royal Society, 1985: 9). In light of the then flourishing PUS movement, Thomas and Durant (1987) reviewed the rationales behind such efforts and summarized nine beneficiaries: science itself, national economies, national power and influence, society as a united whole, individuals’ decision-making and job opportunities, democratic government, intellectual life, aesthetic appreciation, and morality.³

If the understanding and appreciation of science by the public are of such critical importance, why did it take until the 1980s for the PUS movement to begin gaining significant momentum? As Wynne (1992a) noted, the rise of PUS in the UK was seen as “part of the scientific establishment’s anxious response to a legitimation vacuum which threatened the well-being and social standing of science” (p. 38). This vacuum was a result of the dual lack of interest from the government and the public—the public had somewhat lost its trust in scientists amid events like the Windscale accident and later the Mad Cow Disease (BSE) episode, while the government believed that commercially useful research should seek funding primarily from the industry rather than the public sector (Wynne, 1982; Yearley, 2005). At its root, a basic aim of the agenda was to improve the public’s knowledge and thus its support for science. According to Ziman (1991: 99), who served in the committee for the Bodmer Report, the concerns that “came to a head” in the report were the “gap between the world of science and the world at large” and that “in fearful ignorance, they (the public) might even take against science altogether, heedlessly throwing out the baby with the bathwater.”

Among the diverse efforts in early PUS research,⁴ the predominant and traditional approach was using surveys to measure and analyze the public’s “scientific literary” as well as their interest in and attitudes to science (Durant et al., 1989, 1992; Thomas and Durant, 1987), which has often been referred to as a “deficit model” by its critics (e.g. Wynne, 1991, 1992a; see also Horst, 2008; Michael, 2002).⁵ The core assumptions in this approach are that (1) science plays an unquestionably essential (and often positive) role in improving our personal life and social prosperity, (2) there is a deficit in the understanding of science among the public, (3) scientists have the authority and obligation to reduce this lack of knowledge through a one-direction transmission, and (4) the public’s interest in and support to science are related to their knowledge about science, if not always positively. Under the deficit model, major PUS efforts have been devoted to enhancing the public’s scientific literacy,⁶ in which the key forms include science week, popular science magazines, and science museums, and major topics typically fall in the life sciences and biomedicine, such as the safety of genetically modified organisms and vaccination.

While the importance of PUS is often justified with a democratic imperative – namely, that a truly democratic society requires its citizens to be sufficiently scientifically literate in order to effectively participate in public policy-making (Durant et al., 1989; Miller, 1983, 1998) – democracy has played a more fundamental, albeit implicit, role: the PUS initiative is essentially rooted in the deep recognition of citizens’ rights in a democratic society to make decisions about funding for science. The default premise is that, although science might be a superior knowledge enterprise, its development is eventually up to the public support and does not enjoy the prerogative of bypassing citizen deliberation in a democracy, which is not necessarily the case in an authoritarian society. Its mind-set is similar to Merton’s (1942) argument that democracy is the soil and shelter for science because they share similar values. The perceived declining public support for science prompted the PUS initiative to improve scientific literacy and produce publics who can vote for pro-science officials. Democracy was thus seen as the conduit for ensuring scientific autonomy, allocating public funds to research and development, and guarding scientific growth from an “abnormal” state.

Accordingly, the imaginary of model citizens in PUS is that they should be scientifically literate and able to make full use of the benefits of science, and in turn, should support science for the sake of themselves and society. This imaginary is based on two mutually enforcing perceptions of science. Epistemically, scientific knowledge is deemed superior and scientists monopolize the authority to create reliable knowledge. Evaluatively, science is considered overwhelmingly good and should be fully developed. These perceptions entail the vision that citizens have an obligation to gain more scientific knowledge, use it in their daily lives, and trust and support science as electorates in a democratic society (Trachtman, 1981). In the PUS picture, science is like a bright lantern hanging above citizens, who should seek its illumination and undergird it by supplying fuel to keep it fully lighted.⁷

4. PES: Governing science through responsible citizens

The imaginations of what citizens should know and do about science turned to a very different focus with the rise of PES. In light of the widely recognized limitations of the decade-long deficit model in PUS, an alternative agenda with a wholly different mind-set has taken over.⁸ With less sanguine and more reflective perceptions of science, the objective has shifted from “public understanding of” to “public engagement in/with” science. As Miller (2001) suggests, a good start to examine PES is the “Science and Society” report by the UK House of Lords in 2000. Like the Bodmer Report on PUS, this report also grew out of concerns intrigued by “public unease, mistrust, and occasional outright hostility” and “deep anxiety among scientists” (Section 1.1). However, instead of emphasizing the benefits and superiority of science, it began by highlighting science’s inherent uncertainty, along with its potential risks and consequences. As the introduction of the report states (UK House of Lords, 2000; italics my own),

Many people are deeply uneasy about the huge opportunities presented by areas of science, including biotechnology and information technology, which seem to be advancing far ahead of their awareness and assent. (Section 1.1)

Where science is advancing rapidly, however, as is currently the case in genetics, much is uncertain, and there is much room for disagreement among experts. Scientific pronouncements in such areas cannot be relied upon in the same way. (Section 1.4)

When science is applied, the problem of uncertainty may be compounded by issues of ethics, economics, social implications and public acceptability. (Section 1.5)

Under this picture, the “crisis of trust” in science is no longer primarily attributed to the ignorance of the public but to the unpredictability of science itself. The decreased public support of science is seen as a result of reasonable concerns over its rapid and potentially risky advances. The report thus appeals to “improve the dialogue between the two sides” through, in addition to “public understanding of science” activities,⁹ improving communication of uncertainty and risk, and “most importantly, by changing the culture of policy-making so that it becomes normal to bring science and the public into a dialogue about new developments at an early stage” (UK House of Lords, 2000; Section 1.19). A number of activities were recommended to engage the public, including consultations at both national and local levels, deliberative polling, standing consultative panels, focus groups, citizens’ juries, consensus conferences, stakeholder dialogues, Internet dialogues, and government’s foresight programs. Such activities have been widely advocated, experimented with and practiced in the UK and many other western countries such as Denmark, Switzerland, the US, New Zealand, and Australia. In 2014, Public Understanding of Science published a special issue on “Public Engagement” to celebrate the journal’s 20th anniversary and reflect on the PES activities and research, as the field has arguably “moved from research into public understanding to research into public engagement” (Bauer, 2014: 3).

The main approach in this “age of engagement” has shifted from the deficit model to the “deliberation model” (Horst, 2008), focusing on increasing public deliberation on science rather than minimizing public deficits in science. The deliberation model is based on a set of different premises: (1) scientific knowledge is not self-evidently unproblematic, but subject to intrinsic uncertainties and risks; (2) publics have valuable understandings and expertise in technoscientific issues; (3) their insights should be fully considered in the decisions on scientific development and applications; and (4) once publics are fully engaged in such processes, science and technology can proceed in a responsible, acceptable, and prosperous manner. Instead of guarding science through public education, at the core of this approach is governing science through public engagement (Burgess, 2014). It has been widely celebrated as “democratizing” science because the governance of science is now no longer monopolized by a handful of experts but by the whole society.¹⁰

Following this democratic ideal, much scholarship has been devoted to questions of “how” over “why” about public engagement (Stilgoe et al., 2014). Scholars have explored and evaluated various forms to implement the public engagement approach. As Delgado et al. (2011) note, public engagement is very often given as the solution, but it should also be taken as the problem. They employ the case of nanotechnology to explore questions such as “why should we do public engagement,” “who should be involved,” and “where should it be grounded.” While Lezaun and Soneryd (2007) examine technologies of eliciting lay opinions in science policy, Braun and Schultz (2010) identify a typology of various “publics” including the general public, the pure public, the affected public, and the partisan public. Comparing genetically modified food and functional food, Nielsen et al. (2011) investigate how politicians decide when public participation should be initiated and argue that although democratic participation is claimed as a universal value, its initiation is only selective and subject to political games.

As a collective, the PES community holds “a normative commitment to the idea of democratic science policy,” of which public engagement is an important part (Stilgoe et al., 2014: 5). Although this normative commitment is primarily oriented to policy-makers, who are urged to do “the right thing” and engage citizens, it also builds on a normative premise about the responsibility of the general public. In her article “Scientific citizenship in a democratic society,” Árnason (2013) articulates this premise explicitly (italics my own):

The deliberative vision of the scientific citizen does not violate the right to privacy and freedom from politics, but it emphasizes the fact that in a democratic society every citizen is partially responsible for public policy. It is the duty of democratic politicians to conduct politics in such a way that the citizens are well informed and otherwise enabled to assume their responsibilities as democratic citizens which in turn should affect political decisions. (p. 9)

The imaginary of model citizens behind PES is one of responsible citizens, who are supposed to actively take responsibility to engage in policy-making processes about technoscientific issues. It is particularly manifest in discussions about how citizens can best take up this responsible role, such as the notion of “citizen capacity building” (Selin et al., 2017). And democracy serves as a mechanism that ensures science, its applications, and consequences are managed responsibly. This imaginary is founded on revised views of science. The epistemic stance is that scientists and lay people share expertise and credibility for reliable knowledge. From an evaluative perspective, it recognizes that science, fraught with uncertainties and risks, carries varied socio-ethical-economic consequences and thus demands cautious advancement. In contrast to its image in PUS, science has now stepped down from the top and become an object to be interrogated in front of reflective citizens, awaiting cautious deliberation and governance.¹¹

5. CS: Generating science through contributive citizens

Parallel to the development of PES was the emergence of “Citizen Science” projects, especially in the US. It was embedded in yet another set of visions of model citizens, which cast citizens into the process of making science. Although the term “citizen science” has been used in many ways (see Eitzel et al., 2017), this article focuses on CS as defined by Bonney et al. (2016b), namely, “public participation in scientific research, in particular, with members of the public partnering with professional scientists to collectively gather, submit, or analyze large quantities of data” (p. 3). The first CS projects as such date back to the Cornell Lab of Ornithology, which recruited the general public to collect bird data. These were described by Rick Bonney in his 1996 article “Citizen Science: A Lab Tradition.” Over the past two decades, CS projects have emerged in numerous countries and spanned various disciplines, such as space science, geography, oceanography, zoology, and environmental monitoring. International conferences have been held and three major associations were recently established: the Citizen Science Association (CSA), European Citizen Science Association (ECSA), and Australian Citizen Science Association (ACSA). Sponsored by the CSA, the journal Citizen Science: Theory and Practice was founded in 2014 to “provide the space to enhance the quality and impact of citizen science efforts” (Bonney et al., 2016a: 2). Recognizing their potential to promote research and innovation, government bodies such as the US Federal Government and the European Union have begun supporting CS projects under a distinct funding category.

Ottinger (2017: 351) identifies two distinct traditions of CS: “social movement-based citizen science” (initiated by activists) and “scientific authority-driven citizen science” (organized by scientific institutions). The latter will be the focus of this article.¹² A primary goal of these CS projects is to advance scientific knowledge production as legitimated by professional scientists. Primarily serving as volunteer participants, citizens are acknowledged for helping scientists “examine and analyze what would otherwise be unmanageable amounts of information” (Bonney et al., 2016b: 6), thereby “increasing cost effectiveness to maximize the return on taxpayer dollars” (US Congress, 2016). Another major function of CS projects involves science education for the participants, not only in those specifically designed for science curriculum at K-12 and college levels (e.g. Spicer et al., 2020) but also in the general, research-oriented projects (e.g. Bonney et al., 2016b; Trumbull et al., 2000).

The basic approach here is a straightforward “participatory model” that stresses the inclusion of the general public as collaborators of scientists. Various forms of participation have been identified along the scientific research pipeline. For example, the US Federal Government’s “Crowdsourcing and Citizen Science Act of 2016” lists eight ways in which citizens are encouraged to volunteer in scientific research.¹³ Bonney et al. (2016b) identify four categories of CS projects according to the form of participation: data collection projects, data processing projects, curriculum-based projects, and community science projects. And Haklay (2013b) develops a typology of CS based on the level of participation: crowdsourcing, distributed intelligence, participatory science, and extreme citizen science.

In this participatory model, science is viewed instrumentally in two senses: firstly, that science itself is a tool to be improved through citizen participation, and secondly, that citizen science is a useful tool for addressing a number of challenges beyond the reach of traditional scientific enterprise. On one hand, as Lewenstein (2016) observes, CS “provides opportunities for gathering rich data across a much wider geographic range than traditional scientific teams, while also drawing on the unique image-processing capabilities of human brains” (p. 1). On the other hand, many discussions have explored how CS can be best applied as a useful tool to study “large-scale patterns in nature” (Bonney et al., 2009), such as biodiversity and climate change (Dickinson et al., 2010). Government authorities, including the US Federal Government and the Environmental Protection Agency, have also developed various online citizen science toolkits.¹⁴

The democratization thesis has been frequently brought up in recent CS literature. Lewenstein (2016) points out that CS projects often emerge “where challenges of democratization, the needs of science education, and the affordance of science communication have come together” (p. 2). Del Savio et al. (2016) argue that crowdsourcing could enhance the democratization of microbiome research by deepening dialogue between citizens and scientists and by improving citizen control over science agenda setting. Haklay (2013a) questions the claimed democratization of geographic knowledge, which supposedly shift the control over Geographic Information System (GIS) data generation and application from a few experts to many users. While CS is sometimes seen as promoting democratic ideal through agenda-setting (Del Savio et al., 2016), democratization of knowledge production aligns better with CS’s original intent. As the v ision statement at ECSA’s official website writes (italics my own),

Our Vision is that, in 2020, citizens in Europe are valued and empowered as key actors in advancing knowledge and innovation and thus supporting sustainable development in our world. Citizen Science is a recognized, promoted and funded approach to foster scientific literacy and the democratization of scientific expertise, to increase the social relevance and sustainable impact of research and to improve the evidence base for environment, biodiversity and climate change policy in Europe and globally.¹⁵

This statement implies that citizens are included, empowered and valued as knowledge producers, sharing scientific expertise alongside scientists. The value of lay public is recognized in creating reliable knowledge, not only through their labor but also through their skills and local expertise. In the official US definition, CS is described as “a form of open collaboration” (US Congress, 2016), treating members of the public as collaborators equivalent to professional scientists. Democracy’s role in science here is generative since it enables more efficient creation and advancement of knowledge by utilizing lay people’s labor, expertise, and intelligence. As such, the imaginary of model citizens is one of contributive citizens, who are supposed to voluntarily partake in and enjoy creating scientific knowledge under professional scientists’ guidance. Compared to PUS and PES, citizens now stand alongside the scientist with their own distinct gestures, contributing their diverse and unique expertise to collective knowledge-making; despite this, the scientist remains the more significant, authoritative, and central figure.¹⁶

6. Conclusion

While members of the public are pivotal in the discourses of PUS, PES, and CS, their role tends to remain largely silent. Citizens are consistently placed at the center of the stage, yet they are frequently treated as passive entities—destined to be educated to understand science, engaged to govern science, or recruited to conduct science. This might stem from the science-centered or policy-oriented nature of the SPI discussions, where the primary interlocutors are scientists, policy-makers, and social science researchers. Nevertheless, such dialogues could not proceed without a certain imaginary of model citizens. The portrayal of these imagined citizens typically remains both subtle and fragmented, with its pieces reflected in various corresponding images—whether they be distrusted government officials, technocratic policy-makers, or underfunded scientists. On a more positive note, it might manifest in images of science that garners strong public support, science that is democratically selected, or science conducted with robust participation from citizens.

This article represents an initial endeavor to assemble the underlying imaginaries of model citizens associated with three influential SPIs. Although different nations embrace these initiatives to varying degrees, and each initiative has its variations, this research unveils some useful patterns while acknowledging their regional focuses and limitations to certain efforts. In summary, the model citizen in PUS is a literate individual, expected to acquire adequate scientific knowledge, apply it in everyday life, and trust and support science; in PES, a responsible one, taking an active role in the governance and policy-making concerning technoscientific issues; and in CS, a contributive one, anticipated to contribute to and take pleasure in scientific knowledge creation. These imaginaries are the result of a complex of perceptions on the nature and value of science, the role of democracy within scientific pursuits, and the modalities of democracy (Table 1). More specifically, PUS presupposes an electoral democracy where scientifically literate citizens vote on funding for science and decision-making based on science. PES rests on deliberative democracy, in which citizen representatives deliberate on agenda-setting for science and desired futures driven by science. In contrast, CS fosters participatory democracy, empowering citizens to partake in the execution of science and blurring the scientist-citizen boundaries. Each of the three SPIs thus integrates its own democratic ideal, and these forms of democracy are not mutually exclusive, allowing for their co-existence in diverse combinations within democratic societies.

Table 1.

The complexes of imaginaries of model citizens in PUS, PES, and CS.

Science and Public Initiatives	Epistemic perception of science	Evaluative perception of science	Role of democracy in science	Type and site of democracy	Imaginary of model citizens	Example of form/theme
PUS (deficit model)	Scientists monopolize authority in creating reliable knowledge.	Optimistic: Science is good and should be fully developed for the welfare of individuals and society.	Guarding: Democracy is the conduit for scientific funding and autonomy, and justifies scientific literacy.	Electoral democracy in scientific funding and decision-making.	Literate Citizens: should gain more scientific knowledge, use it in daily life, and trust and support science.	Science cafe/vaccination.
PES (deliberation model)	Scientists and lay people both have credible insights, with each having their own expertise.	Reflective: Science is full of uncertainties and risks with various socio-economic-cultural consequences, and should thus be carefully developed.	Governing: Democracy is the mechanism through which technoscientific research and its applications are governed in a socially responsible way.	Deliberative democracy in the agenda-setting of science.	Responsible Citizens: should take their responsibility to engage in policy-making processes about technoscientific issues.	Consensus conference/ nanotechnology.
CS (participatory model)	Scientists and lay people share expertise and credibility in creating reliable knowledge.	Instrumental: Science is a tool that can be improved through citizen participation, especially for large-scale issues.	Generating: Democracy is a useful approach to efficiently create knowledge by absorbing lay labor, expertise, and intelligence.	Participatory democracy in the execution of science.	Contributive Citizens: should partake in and enjoy creating scientific knowledge.	Crowdsourcing/ornithology.

PUS: public understanding of science; PES: public engagement in science; CS: citizen science.

The boundaries between PUS, PES, and CS are not absolute. Continuities exist between PUS and PES, as the enduring deficit model implies (Irwin, 2006; Simis et al., 2016); overlaps between PES and CS can emerge, particularly in social movement-based citizen science (Irwin, 1995); and CS and PUS share a distant kinship, given CS’s often lauded contribution to science education (Bonney et al., 2016b). These imaginaries and their corresponding perception-complexes are neither entirely confined to their respective initiatives nor incompatible with each other; instead, they intersect, complement, and even reinforce each other. For instance, a responsible citizen is usually expected to be literate and is more likely to be contributive. Thus, rather than offering a rigid, precise, and comprehensive categorization of the three SPIs, this article proposes a heuristic typology to help understand the envisaged normative roles of citizens in a scientific, democratic society, along with their underlying assumptions about science and democracy.

As technoscientific advancements continue, the interplay between emerging technologies, democracy, and citizen involvement will consistently feature in discussions among policy-makers, entrepreneurs, technologists, and social scientists alike. These pressing issues can draw insights from, as well as enrich, the intricate imaginaries of model citizens gleaned from years of work in PUS, PES, and CS. This article’s framework provides a starting point to unravel themes like the democratization of artificial intelligence (AI), recurrent in recent media, industry, and academic discourses (e.g. Coy, 2023; Hasbe and Lippert, 2020; Sudmann, 2019). Meanwhile, global concerns over ethical AI governance have prompted numerous guidelines and policies across many countries and international organizations. This provides an excellent site to explore the cultural and institutional dynamics that shape imaginaries of the roles of citizens and experts (e.g. Hilgartner et al., 2017; Irwin, 2006; Jasanoff, 2011) and invites further empirical research.

Footnotes

Acknowledgements

The author would like to thank Stephen Hilgartner for his insightful advice, and the editor and anonymous reviewers for their valuable feedback, which improved this article. He also gratefully acknowledges the support and encouragement of Bruce Lewenstein, Ranjit Singh, and Tinakrit Sireerat, as well as the organizers of the 2022 IAMCR Suzhou Pre-conference and Harvard University’s Program on Science, Technology and Society, which were instrumental during the initial phases of this research.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Wanheng Hu

Notes

Author biography

Wanheng Hu is a PhD candidate in Science and Technology Studies at Cornell University, where he is also doing a graduate minor in Media Studies. His research lies at the intersection of social studies of science and technology, medical sociology, critical data/algorithm studies, and public engagement with science. His dissertation project ethnographically examines the cultivation of credible machine learning systems in expert practices, with an empirical focus on image-based diagnostics within the Chinese medical artificial intelligence industry.

References

Anderson

(1991) Imagined Communities: Reflections on the Origin and Spread of Nationalism. New York, NY: Verso Books.

Appadurai

(1996) Modernity at Large: Cultural Dimensions of Globalization. Minneapolis, MN: University of Minnesota Press.

Árnason

(2013) Scientific citizenship in a democratic society. Public Understanding of Science 22(8): 927–940.

Barnett

Burningham

Walker

Cass

(2012) Imagined publics and engagement around renewable energy technologies in the UK. Public Understanding of Science 21(1): 36–50.

Bauer

(2014) A word from the editor on the special issue on “public engagement.” Public Understanding of Science 23(1): 3.

Bauer

Allum

Miller

(2007) What can we learn from 25 years of PUS survey research? Liberating and expanding the agenda. Public Understanding of Science 16(1): 79–95.

Bonney

(1996) Citizen science: A lab tradition. Living Bird 15(4): 7–15.

Bonney

Cooper

Ballard

(2016a) The theory and practice of citizen science: Launching a new journal. Citizen Science: Theory and Practice 1(1): 1–4.

Bonney

Cooper

Dickinson

Kelling

(2009) Citizen science: A developing tool for expanding science knowledge and scientific literacy. BioScience 59(11): 977–984.

10.

Bonney

Phillips

Ballard

Enck

(2016b) Can citizen science enhance public understanding of science? Public Understanding of Science 25(1): 2–16.

11.

Braun

Schultz

(2010) “. . . a certain amount of engineering involved”: Constructing the public in participatory governance arrangements. Public Understanding of Science 19(4): 403–419.

12.

Burgess

(2014) From “trust us” to participatory governance: Deliberative publics and science policy. Public Understanding of Science 23(1): 48–52.

13.

Calderini

Simone

(2021) Give citizens a seat at the table. Nature Italy. Available at: https://www.nature.com/articles/d43978-021-00038-1

14.

Chilvers

Kearnes

(eds) (2016) Remaking Participation: Science, Environment and Emergent Publics. London: Routledge.

15.

Christiano

(2018) Democracy. In: Zalta

(ed.) The Stanford Encyclopedia of Philosophy, Fall 2018 edn. Stanford, CA: Metaphysics Research Lab, Stanford University. Available at: https://plato.stanford.edu/archives/fall2018/entries/democracy/ (accessed 19 March 2021).

16.

Coates

(2022) 1992: The first issue of public understanding of science. Public Understanding of Science 31(3): 340–345.

17.

Coy

(2023) an A.I. and democracy fix each other? The New York Times, 5 April. Available at: https://www.nytimes.com/2023/04/05/opinion/artificial-intelligence-democracy-chatgpt.html (accessed 4 July 2023).

18.

Del Savio

Prainsack

Buyx

(2016) Crowdsourcing the Human Gut. Is crowdsourcing also “citizen science”? Journal of Science Communication 15(3): A03.

19.

Delgado

Lein Kjølberg

Wickson

(2011) Public engagement coming of age: From theory to practice in STS encounters with nanotechnology. Public Understanding of Science 20(6): 826–845.

20.

Dickinson

Zuckerberg

Bonter

(2010) Citizen science as an ecological research tool: Challenges and benefits. Annual Review of Ecology, Evolution, and Systematics 41(1): 149–172.

21.

Durant

Evans

Thomas

(1989) The public understanding of science. Nature 340(6228): 11–14.

22.

Durant

Evans

Thomas

(1992) Public understanding of science in Britain: The role of medicine in the popular representation of science. Public Understanding of Science 1(2): 161–182.

23.

Eitzel

Cappadonna

Santos-Lang

Duerr

Virapongse

West

, et al. (2017) Citizen science terminology matters: Exploring key terms. Citizen Science: Theory and Practice 2(1): 11–20.

24.

Gallie

(1955) Essentially contested concepts. Proceedings of the Aristotelian Society 56: 167–198.

25.

Goven

(2003) Deploying the consensus conference in New Zealand: Democracy and de-problematization. Public Understanding of Science 12(4): 423–440.

26.

Guay

Birch

(2022) A comparative analysis of data governance: Socio-technical imaginaries of digital personal data in the USA and EU (2008–2016). Big Data & Society 9(2): 1–13.

27.

Haklay

(2013a) Citizen science and volunteered geographic information: Overview and typology of participation. In: Sui

Elwood

Goodchild

(eds) Crowdsourcing Geographic Knowledge: Volunteered Geographic Information (VGI) in Theory and Practice. Dordrecht: Springer, pp. 105–122.

28.

Haklay

(2013b) Neogeography and the delusion of democratisation. Environment and Planning A: Economy and Space 45(1): 55–69.

29.

Hasbe

Lippert

(2020) Democratization of machine learning and artificial intelligence with Google Cloud. In: Google Cloud Blog. Available at: https://cloud.google.com/blog/products/data-analytics/democratization-of-ml-and-ai-with-google-cloud (accessed 4 July 2023).

30.

Hess

(2015) Publics as threats? Integrating science and technology studies and social movement studies. Science as Culture 24(1): 69–82.

31.

Hilgartner

Prainsack

Hurlbut

(2017) Ethics as governance in genomics and beyond. In: Felt

Fouche

Miller

, et al (eds) Handbook of Science and Technology Studies. Cambridge, MA: MIT Press, pp. 823–851.

32.

Horst

(2007) Public expectations of gene therapy: Scientific futures and their performative effects on scientific citizenship. Science, Technology, & Human Values 32(2): 150–171.

33.

Horst

(2008) In search of dialogue: Staging science communication in consensus conferences. In: Cheng

Claessens

Gascoigne

, et al (eds) Communicating Science in Social Contexts: New Models, New Practices. Dordrecht: Springer, pp. 259–274.

34.

Irwin

(1995) Citizen Science: A Study of People, Expertise and Sustainable Development. New York, NY: Routledge.

35.

Irwin

(2001) Constructing the scientific citizen: Science and democracy in the biosciences. Public Understanding of Science 10(1): 1–18.

36.

Irwin

(2006) The politics of talk: Coming to terms with the “new” scientific governance. Social Studies of Science 36(2): 299–320.

37.

Irwin

(2014) From deficit to democracy (re-visited). Public Understanding of Science 23(1): 71–76.

38.

Irwin

(2015) Citizen science and scientific citizenship: Same words different meanings? In: Schiele

Marec

Baranger

(eds) Science Communication Today: Current Strategies and Means of Action-2015. Nancy: Presses Universitaires de Nancy, pp. 29–38.

39.

Jasanoff

(2011) Designs on Nature: Science and Democracy in Europe and the United States. Princeton, NJ: Princeton University Press.

40.

Jasanoff

(2015) Future imperfect: Science, technology, and the imaginations of modernity. In: Jasanoff

Kim

S-H

(eds) Dreamscapes of Modernity: Sociotechnical Imaginaries and the Fabrication of Power. Chicago, IL: University of Chicago Press, pp. 1–33.

41.

Jasanoff

Kim

S-H

(2009) Containing the atom: Sociotechnical imaginaries and nuclear power in the United States and South Korea. Minerva 47(2): 119–146.

42.

Joly

P-B

(2015) Governing emerging technologies? The need to think outside the (black) box. In: Hilgartner

Miller

Hagendijk

(eds) Science and Democracy: Making Knowledge and Making Power in the Biosciences and Beyond. New York, NY: Routledge, pp. 133–155.

43.

Kinchy

(2017) Citizen science and democracy: Participatory water monitoring in the Marcellus shale fracking boom. Science as Culture 26(1): 88–110.

44.

Laugksch

(2000) Scientific literacy: A conceptual overview. Science Education 84(1): 71–94.

45.

Lewenstein

(2016) Can we understand citizen science? Journal of Science Communication 15(1): E1–E5.

46.

Lezaun

Soneryd

(2007) Consulting citizens: Technologies of elicitation and the mobility of publics. Public Understanding of Science 16(3): 279–297.

47.

Marres

(2016) Material Participation: Technology, the Environment and Everyday Publics. London: Springer.

48.

Marris

(2015) The construction of imaginaries of the public as a threat to synthetic biology. Science as Culture 24(1): 83–98.

49.

McNeil

Arribas-Ayllon

Haran

Mackenzie

Tutton

(2016) Conceptualizing imaginaries of science, technology, and society. In: Felt

Fouche

Miller

, et al (eds) The Handbook of Science and Technology Studies, 4th edition. Cambridge, MA: MIT Press, pp. 435–463.

50.

Merton

(1942) A note on science and democracy. Journal of Legal and Political Sociology 1: 115–126.

51.

Michael

(2002) Comprehension, apprehension, prehension: Heterogeneity and the public understanding of science. Science, Technology, & Human Values 27(3): 357–378.

52.

Miller

(1983) Scientific literacy: A conceptual and empirical review. Daedalus 112(2): 29–48.

53.

Miller

(1998) The measurement of civic scientific literacy. Public Understanding of Science 7(3): 203–223.

54.

Miller

(2001) Public understanding of science at the crossroads. Public Understanding of Science 10(1): 115–120.

55.

Nielsen

Lassen

Sandøe

(2011) Public participation: Democratic ideal or pragmatic tool? The cases of GM foods and functional foods. Public Understanding of Science 20(2): 163–178.

56.

Nowotny

(2014) Engaging with the political imaginaries of science: Near misses and future targets. Public Understanding of Science 23(1): 16–20.

57.

Ottinger

(2017) Reconstructing or reproducing? Scientific authority and models of change in two traditions of citizen science. In: Tyfield

Lave

Randalls

, et al. (eds) The Routledge Handbook of the Political Economy of Science. New York, NY: Routledge, pp. 351–363.

58.

Royal Society (1985) Public Understanding of Science. London: Royal Society.

59.

Rudek

(2022) Capturing the invisible. Sociotechnical imaginaries of energy. The critical overview. Science and Public Policy 49(2): 219–245.

60.

Sadowski

Bendor

(2019) Selling smartness: Corporate narratives and the smart city as a sociotechnical imaginary. Science, Technology, & Human Values 44(3): 540–563.

61.

Selin

Rawlings

de Ridder-Vignone

Sadowski

Allende

Gano

, et al. (2017) Experiments in engagement: Designing public engagement with science and technology for capacity building. Public Understanding of Science 26(6): 634–649.

62.

Simis

Madden

Cacciatore

Yeo

(2016) The lure of rationality: Why does the deficit model persist in science communication? Public Understanding of Science 25(4): 400–414.

63.

Sismondo

(2020) Sociotechnical imaginaries: An accidental themed issue. Social Studies of Science 50(4): 505–507.

64.

Smith

(2009) Imaginaries of development: The Rockefeller foundation and rice research. Science as Culture 18(4): 461–482.

65.

Spicer

Nadolny

Fraser

(2020) Going squirrelly: Evaluating educational outcomes of a curriculum-aligned citizen science investigation of non-native squirrels. Citizen Science: Theory and Practice 5(1): 141–113.

66.

Stephens

Atkinson

Glasner

(2013) Institutional imaginaries of publics in stem cell banking: The cases of the UK and Spain. Science as Culture 22(4): 497–515.

67.

Stilgoe

Lock

Wilsdon

(2014) Why should we promote public engagement with science? Public Understanding of Science 23(1): 4–15.

68.

Sudmann

(ed.) (2019) The Democratization of Artificial Intelligence: Net Politics in the Era of Learning Algorithms. Bielefeld: Transcript Publishing.

69.

Taylor

(2002) Modern social imaginaries. Public Culture 14(1): 91–124.

70.

Thomas

Durant

(1987) Why should we promote the public understanding of science? In: Shorland

(ed.) Scientific Literacy Papers. Oxford: Rewley House, pp. 1–14.

71.

Trachtman

(1981) The public understanding of science effort: A critique. Science, Technology, & Human Values 6(3): 10–15.

72.

Trumbull

Bonney

Bascom

Cabral

(2000) Thinking scientifically during participation in a citizen-science project. Science Education 84(2): 265–275.

73.

UK House of Lords (2000) Science and Society (Report of House of Lords, Select Committee on Science and Technology, chair Patrick Jenkin; HL Paper 38). London: The Stationery Office.

74.

US Congress (2016) Crowdsourcing and citizen science act of 2016 (15 USC 3724). Available at: https://www.congress.gov/bill/114th-congress/house-bill/6414/text (accessed 5 December 2021).

75.

Vincent

(2014) The politics of buzzwords at the interface of technoscience, market and society: The case of “public engagement in science.” Public Understanding of Science 23(3): 238–253.

76.

Warf

Sui

(2010) From GIS to neogeography: Ontological implications and theories of truth. Annals of GIS 16(4): 197–209.

77.

Welsh

Wynne

(2013) Science, scientism and imaginaries of publics in the UK: Passive objects, incipient threats. Science as Culture 22(4): 540–566.

78.

Wynne

(1982) Rationality and Ritual: The Windscale Inquiry and Nuclear Decisions in Britain. London: British Society for the History of Science.

79.

Wynne

(1991) Knowledges in context. Science, Technology, & Human Values 16(1): 111–121.

80.

Wynne

(1992a) Misunderstood misunderstanding: Social identities and public uptake of science. Public Understanding of Science 1(3): 281–304.

81.

Wynne

(1992b) Public understanding of science research: New horizons or hall of mirrors? Public Understanding of Science 1(1): 37–43.

82.

Wynne

(2006) Public engagement as a means of restoring public trust in science—Hitting the notes, but missing the music? Public Health Genomics 9(3): 211–220.

83.

Yearley

(2005) Making Sense of Science: Understanding the Social Study of Science. London: Sage.

84.

Ziman

(1991) Public understanding of science. Science, Technology, & Human Values 16(1): 99–105.