Content uploaded by Monika Taddicken
Author content
All content in this area was uploaded by Monika Taddicken on Sep 14, 2023
Content may be subject to copyright.
JCOM
Deconstructing citizen science: a framework on
communication and interaction using the concept of roles
Susanne Hecker and Monika Taddicken
Citizen science opens the scientific knowledge production process to
societal actors. In this novel collaboration process, scientists and citizens
alike face the challenge of new tasks and functions, eventually resulting in
changing roles. Role theory provides a way of conceptualizing the roles
that people take in communication and interaction. We use role theory to
create a framework that identifies scientists’ and citizens’ tasks in citizen
science projects, main aims of communication, spaces they interact in, and
their roles — thus providing a structured way to capture communication
and interaction in and about CS for further scientific reflection and practical
application.
Abstract
Citizen science; Science communication: theory and modelsKeywords
https://doi.org/10.22323/2.21010207DOI
Submitted: 19th October 2021
Accepted: 6th January 2022
Published: 14th March 2022
Communication
and interactions in
citizen science
Foundations
Citizen science (CS) projects provide a wide range of opportunities for engagement
between members of the public and scientists [Haklay, 2013; Shirk et al., 2012].
Wiggins and Crowston [2015, p. 1] emphasise this collaboration in their definition
of CS as “a form of research collaboration that involves volunteers in producing
authentic scientific research”. Further, CS changes the communication and
interaction between actors: as the roles of lay people and experts in knowledge
production and use change [Bonfadelli et al., 2017; Turnhout et al., 2013], new
modes of communication appear.
The term CS originated from two different epistemological directions: those by
Irwin [1995] and Bonney [1996]. While Irwin defines CS as an approach to support
democratic and participatory science, and focuses on the active citizens who work
collaboratively with scientists to create knowledge, Bonney describes it mainly as a
tool used by professional scientists where volunteering citizens contribute to
Article Journal of Science Communication 21(01)(2022)A07 1
science through environmental data collection. In this paper, we make use of both
CS definition strands.
While science and society have historically been seen as separate entities [Burns
and Medvecky, 2018], the boundaries between scientific and civic actors begin to
blur in CS. Here, members of the public are neither an external audience for
communication, nor actors of scholarly communication within the academic
community, but rather part of the project itself. Thus, CS differs from engaging the
public in events like a science festival or edutaining formats such as science slams
where the public is still seen as an external audience.
Communication and interaction processes in CS are a key element for the
collaboration process between scientists and project participants, and are
inseparable from the scientific activities. While communication to external
audiences might be an addition to the science activities in research projects without
public engagement, completely abstaining from outreach activities would not
necessarily put the scientific endeavour at any risk. In contrast, both
communication to external as well as project-internal audiences must be effective
to enable scientific activities in CS [Riesch, Potter and Davies, 2013], for example by
attracting participants and exchanging knowledge between project partners to
fulfil the various project tasks.
Communication in CS may refer to two different meanings: communication as a
process and as a tool. As a process, communication builds a joint understanding
between actors of the reality of their collaboration. Thus, in a fundamental sense,
communication encompasses interaction and collaboration, dialogue and
reciprocal understanding, and negotiations [van der Sanden and Meijman, 2008].
In an expedient sense, strategic use of communication as a tool is used to convey
the exchange of information; for example, to reach a target audience, to motivate
participants’ discussion of methods and data collection, to negotiate interests and
motivation, to provide feedback, or to communicate results [e.g., Davis et al., 2018].
The actors in CS are members of different communities, namely the professional
scientific and the non-professional, and they collaborate and interact to various
degrees depending on the project. To achieve this, they must reach a degree of
mutual understanding, which might be a complex process as both parties live and
act within their own cultures, as described in Caplan’s “Two-Communities Theory”
[Caplan, 1979]: sometimes, differing logics and aims define these cultures, and thus
understandings and perceptions may differ, including expectations, languages and
jargon, value systems, motivations, and aims for research and participation.
A CS project that illustrates the aforementioned challenge was the Chicago Area
Pollinator Study. The goals of this project were to promote wildlife-friendly
behaviour changes, improve participants’ attitudes towards pollinators, and
increase knowledge about bees, urban habitats, and science [Druschke and Seltzer,
2012]. Unfortunately, the design dynamic was more instructive than collaborative,
and while participants did learn more statistically, it was not much of an actual
improvement. Interest was not improved either. According to the authors, the
failure was because they neglected to truly bring citizen scientists into the whole
collaborative effort. The authors attribute this failure to focusing too much on the
data they could get from participants instead of focusing on and handling
https://doi.org/10.22323/2.21010207 JCOM 21(01)(2022)A07 2
participants’ needs and expectations which resulted in miscommunication. Other
CS project coordinators also report this challenge [e.g. Ramirez-Andreotta et al.,
2015]. In their recommendations for other CS projects, Druschke and Seltzer [2012]
emphasise the necessity to consider participants’ views and needs and also to
maintain active communication and exchange between scientists and participants.
Role theory
According to role theory, an individual has various roles in life that come with
expectations on how to behave and this is associated with a particular social status
[Linton, 1936; Miebach, 2010; van der Horst, 2016]. Role theory is not restricted to
specific research fields and is applied in, for example, political sciences [Klose,
2019], the processes of identity construction in business [Simpson and Carroll,
2008], and sex/gender or family research or international relations [van der Horst,
2016].
Role theory, as a way of conceptualizing the roles that people take in
communication and interactions, offers a useful structure for capturing the
complex interactions in CS. This can be explained by two main strands of role
concept that are relevant for our considerations: interactionist role theory and
normative-structural role concept. The former, interactionist role theory, considers
role to be a manifestation of reciprocal interactions with others [Turner, 1985].
As such, role is seen not as a fixed concept or set of norms and expectations by
society, but as flexible, adopted, and acted out through individuals in dynamic
social processes [van der Horst, 2016; Yodanis, 2003] and is therefore only
temporary [Krappmann, 1971].
In the latter, normative-structural role concept, an individual’s role is defined by
their position or function in social order, such as profession or membership, which
is linked to certain rights and privileges, duties, and obligations. Roles come with
specific rules for interactions [Linton, 1936; Merton, 1957; Miebach, 2010] and role
actors are expected to more or less abide by them in given situations [Mead, 1934]
in order to support and structure the social culture [Lynch, 2007]. Additionally,
within the normative-structural role concept, the status of these actors in the social
order is linked to their position on the ladder of appreciation and prestige in
society [Dahrendorf, 2010].
In CS, new modes of communication and interaction emerge at the micro and meso
level, due to a change in the relationship and roles of actors. The micro level refers
to interactions of individuals and their roles on a small scale like in a CS initiative.
The meso level considers groups of actors and their communication and interaction
and how roles change at a structural level. For example, at the micro level, actors
might take up roles that had previously not been open or routine to them:
members of the public may become project initiators, and scientists may become
motivators for participation. On the meso-level, traditional role concepts are
challenged, as previously the public had a more passive, receiving role as the
audience [Loosen and Schmidt, 2012; Neuberger et al., 2019]. Now, the public
assumes a performance role in the knowledge production process, while the
scientists’ role might have shifted from that of a knowledge producer to facilitator
[Neuberger et al., 2019]. Engaging with members of society in the research process
https://doi.org/10.22323/2.21010207 JCOM 21(01)(2022)A07 3
also means allowing for new questions and different forms of knowledge to be
included, which may result in the questioning of science’s privileged status and
sovereignty over public knowledge.
The new or shifting roles in CS have resulted in novel interaction and
communication processes between actors for which there is very little research.
Science communication research has responded to this by shifting to a conceptual
and overarching perspective when looking at CS [Lewenstein, 2016]. However,
while the literature on CS practice and outcomes is increasing exponentially
[Kullenberg and Kasperowski, 2016; Meyer and Sandøe, 2012], analyses of the
actual nature of communication and interaction processes among actors in CS is
scarce. In order to change the perception of science communication as a
unidirectional dynamic, communication research on CS should focus more on
actors’ new or shifting roles in CS.
In this paper, we contribute to the understanding of communication and
interaction processes in CS by (1) providing a conceptual framework of CS
communication and interaction processes and roles, (2) adapting the concept of
role theory to roles in CS and by (3) exploring the interactions and roles at the
micro and meso level, and the interaction space that CS creates for actors.
Framework on
citizen science
communication
and interaction
The aim of this framework is to distinguish between different aspects of interlinked
CS communication processes and interactions. These aspects include for example,
the involvement of actors, communication aims, and potential roles related to the
micro and meso-level. This framework can be useful, firstly, in stimulating
scientific discussion and reflection on understanding and interpretation of
communication, interaction, and roles in CS projects. Secondly, to develop concrete
measures of communication by identifying tasks and responsibilities according to
project aims. The framework thus also targets CS project coordinators and science
communication practitioners.
The main purpose of the framework on communication and interaction presented
in this section is to structure the challenges and potential benefits that might result
from scientists and the participating public having a different understanding of
involvement, tasks or roles.
Based on a classification of CS projects, we conceptualise our framework on CS
communication and interaction processes as the combination of aspects at the
micro and meso-level of CS.
The factors of this framework are deviated from citizen science scholarly literature
and subsequently complemented by factors that relate to role theory. They address
basic questions: what type of citizen science project is it? Who is involved? What is
the involvement and which tasks do the actors have? These factors are the basis for
Shirk’s et al. [2012] categorisation of CS projects — whereby we explicitly address
the factor of different actors involved where Shirk et al. and other scholars solely
reflect on citizen scientists and their involvement in projects. The factor of main
aims of communication is included as it implicates main underlying, explicit or
non-explicit drivers for communication and interaction. The factors of interaction
space and potential role are taken from role theory as relevant main categories.
https://doi.org/10.22323/2.21010207 JCOM 21(01)(2022)A07 4
Scholarly literature from the field of citizen science supports the framework’s
factors. In addition, we have applied a strand of theory not previously considered,
namely role theory. The literature search was done to the point of theoretical
saturation, i.e. until adding additional data didn’t contribute any more properties
to our categories.
CS project type and actors involved
CS projects are complex and have multiple layers such as research area,
involvement, project targets, and expected outcomes [Bonney et al., 2016], and a
single framework cannot express them all. As such, we have narrowed the
framework to include only those, which are relevant for our purposes.
Communication and interaction in CS varies according to the degree of
collaboration in any given CS initiative [Wagenknecht et al., 2021]. To better assess
the different levels of engagement, Shirk et al. [2012] have proposed a framework
for CS projects focussing on participants’ levels of involvement, which we apply as
the basic category to our framework.
Shirk et al. [2012] introduced three main project types which are: a) contributory
projects, “which are generally designed by scientists and for which members of the
public primarily contribute data”, b) collaborative projects, “which are generally
designed by scientists and for which members of the public contribute data but
also help to refine project design, analyse data, and/or disseminate findings”, and
c) co-created projects, “which are designed by scientists and members of the public
working together and for which at least some of the public participants are actively
involved in most or all aspects of the research process” [Shirk et al., 2012]. We add
the additional category of d) community-led projects where community members
reach out to scientists with an issue of mostly local concern to the community and
are the main drivers in the CS research process [Haklay, 2015; Roy et al., 2012; Shirk
et al., 2012]. These projects have gained increasingly more attention in CS and add
to the spectrum of levels of engagement (Table 1, column 1).
Whereas CS project type overviews often focus on citizens, our framework goes
further by considering scientists as actors (column 2). This addition allows us to
examine the scientist-citizen interaction, namely, the level of involvement and tasks
of the two groups of actors (column 3), and the main aims of communication
(column 4). These categories are related to the project level, which is the
micro-level of CS.
We acknowledge that many different stakeholders play a role in CS projects. For
example educators, communicators, community managers, societal organisations,
policy makers, and (local) government. All of these actors might be considered
when expanding the framework, however to lay the foundation of a basic
understanding, we limit our scope to the two core actor groups of CS initiatives.
We apply role theory on the meso level and include the categories of ‘interaction
space’ (column 5) and ‘potential roles’ (column 6) to allow for reflections on a more
abstract level.
https://doi.org/10.22323/2.21010207 JCOM 21(01)(2022)A07 5
Micro-level communication and interaction
Involvement and tasks
Actors in CS are involved at varying degrees and with different tasks, which can be
associated with different roles at the project level (Table 1, column 3 and 6). For
example, the tasks and role of project manager, of data provider and analyst or the
role of researcher and communicator must be defined and negotiated where
necessary according to the project design, goals, and the level of actor involvement.
It is important to emphasize that expectations of the role actors are vital in this
context.
In ideal project management, the individual best able to perform a particular
function and task would fill the pertinent role. The range of tasks is manifold in CS
projects and is defined by the individual project design and management.
Therefore, in CS research, it is important to recognise what skills are needed to
fulfil a task such as data collection; does a citizen already possess the necessary
skills, or could they be trained to acquire them? Whose responsibility is data
quality management in a CS project? For example, the person collecting and
providing data in a contributory project will need to fulfil certain standards and
follow a protocol, and this may or may not require certain skills and expertise, but
it certainly requires the physical ability to handle the task. The main qualification
to fulfil a task therefore depends on the competencies and abilities a role actor has
rather than their institutional educational or societal status.
Main aims of communication and interaction
Aims of communication are linked to the motivations and goals of communication
actors in CS. Participants’ main aims of communication might change over time
and differ from those of researchers or other groups of participants [Geoghegan
et al., 2016; Hobbs and White, 2012]. Citizen scientists might be more interested in
solving issues of daily relevance than learning about scientific problems, methods
and research questions — something that professional researchers might assume as
the main motivation of participants. According to the different levels of
engagement, the aims of communication can be described more systematically:
contributory projects with the main aim of data collection and processing follow a
“knowledge-first” approach, and co-created projects are more “process-oriented”.
These orientations will define the aims and means of communication between
actors in the projects.
To handle the tasks in CS projects, to address and negotiate main aims of
communication, to create and shape the spaces communication actors interact in
and discuss their roles in the collaborative process, communication as a tool is an
important element. Communication in CS should enable and support both
collaboration and the transfer of information and knowledge between actors, and
should enhance mutual understanding.
The framework section “Main aims of communication and interaction” can create
added value for practical communication in CS. This section helps to identify the
specific main aims of communication according to the appropriate type of citizen
https://doi.org/10.22323/2.21010207 JCOM 21(01)(2022)A07 7
and scientist engagement. Thus, it becomes evident that differences are likely to
occur in terms of aims of communication — not only depending on the type of
engagement, but also depending on the actor.
Meso-level communication and interaction
Interaction space and decision-making
When scientists and members of the public cooperate in the CS research process,
they create a common space for understanding and exchange through their
interaction and communication. Within this metaphoric space, actors in CS rethink
and establish roles and status, responsibilities, values and power relations. The
actors’ own social reality including social identities and roles are co-constructed.
The possibility as well as the need to question and (re-) negotiate assumptions,
expectations, and goals by all actors is particularly given in co-created CS projects
where partners have an equal say in project decision-making processes. It is
therefore necessary to find a common language and to negotiate interests,
motivations and interaction. This process of negotiation and decision-making
provides opportunities to create sense and meaning between participating actors.
Communication therefore is not only the tool for creating reciprocal understanding,
but it is also the process. Theoretically, in the most effective CS projects, all people
involved are intellectual partners and contributors or collaborators. Thus, CS
initiatives challenge the historic divisions between who is part of science and who
is not is [Pandya, 2018].
The idea of an interaction space in relation to CS has already been discussed in
research. This space is created by the encounter between members of the public
and scientists for dialogue and collaboration, knowledge production, and social
learning. Wittmayer and Schäpke [2014] use this metaphor to describe
participatory approaches in sustainability research, which includes an
understanding of activities and roles of actors. Alternative names used in a number
of research fields are also given by the authors: transition area and protected space
[Loorbach, 2010], arena for dialogue [Greenwood, Whyte and Harkavy, 1993], and
communicative or participatory space [Sinwell, 2012; Wicks and Reason, 2009].
One of the most essential parts of the negotiations within the participatory project
interaction space, including CS, is related to power relations and ownership within
the project. This is an aspect that needs more attention in the scientific discussion of
CS [Haklay, 2018; Liboiron, 2019]. Actors need to decide on key points [Bergold
and Thomas, 2010] as CS researchers can learn from process-oriented research
approaches like participatory research. Here scientists and members of society
collaboratively research and influence social realities with the aim of better
understanding and changing them [von Unger, 2014]. Stoecker [1999] emphasises
who takes control of certain steps of decision-making needs to be defined, for
example defining the research question and research process, implementing the
design, analysing the data, reporting, and acting on the results. Concerning a more
conventionally organised, institutionally managed project, and with respect to the
call for more co-created CS projects, collaboration would mean sharing or
devolving power and ownership for the scientist and gaining power and
ownership for citizens. These questions seem to be highly relevant for CS as they
https://doi.org/10.22323/2.21010207 JCOM 21(01)(2022)A07 8
influence the communication and interaction processes, and subsequent potential
roles of actors.
The term ‘interaction space’ is introduced in the framework (Table 1, column 5) to
refer to the interaction capacity, or metaphoric space for actors. Depending on the
different types of CS projects and engagement levels of citizens and scientists,
operational dimensions can define the interaction space: the frequency and
intensity of collaboration, as well as spatial and technical dimensions. The
frequency and intensity of collaboration tells us how often citizens and scientists
meet during the joint engagement in a project. This meeting frequency can range
from a single event to regular meetings over months and even years, for example in
long-term monitoring projects or community-science collaborations. The spatial
dimension refers to the place where the actors meet. These encounters can happen
in an online or virtual space, thus not necessarily implying a synchronous meeting
between the actors. At the other end of the spectrum are face-to-face meetings in a
physical location, most likely in co-created or community-led projects where
communication and interaction are more intense, to discuss e.g. the development
of the project, to negotiate interests or for decision-making processes.
Communication and interaction thereby can be direct or indirect. For example,
scientists could provide instructions or teaching material for participants in an
online platform that participants can retrieve whenever they need. Digital
technologies are drivers for CS as communication and data collection tools.
However, these tools and technology call for their own forms of communication
including possibilities and limitations [Newman, Wiggins et al., 2012; Brenton
et al., 2018; Mazumdar et al., 2018].
Roles of researcher and citizen
Finally, the framework provides the opportunity to reflect on the role of researcher
and citizen both on the micro and meso level (Table 1, column 6).
On the CS project or micro level, the interaction between scientists and citizens
might need flexible role making as well as role taking to achieve common goals,
especially in projects where tasks differ, as roles might need to be adapted or new
ones taken. This flexible role taking and making, according to the interactionist
school of thought, means that an individual has to behave in situations and
interactions that are uncertain and thus not only need taking the perspective of
others to predict actions and expectations and act accordingly, but also make a role.
This includes a flexible approach in social interaction. Callero [1994] speaks of
using roles as social resources in different ways for different purposes. Adaptable
transitions such as this are seen in practice where, for example, a contributory
project might develop to include more collaborative activities or educational
collaboration, such as the Galaxy Zoo project, where participants were initially
asked to analyse data on galaxies, and at a later stage, asked to write a joint
scientific paper which is a much more complex task requiring other skills and
capacities and lead to a different role [Crowston, Mitchell and Østerlund, 2019].
When taking up new roles, actors require mental flexibility and creativity [Lynch,
2007], which might cause problems at the individual level and in relation to other
role actors. Anecdotal evidence has shown that actors in CS do experience role
conflicts [e.g. Salmon et al., 2021]. However, scientific literature in the field of
https://doi.org/10.22323/2.21010207 JCOM 21(01)(2022)A07 9
science communication has not yet provided systematic consideration of role
conflicts. Role conflicts could be imagined easily: consider a citizen who is also a
scientist, though in another field, who is a nature-lover and a family member. If
this individual assumes the role of data provider in a CS project, the citizen’s other
roles can become less salient and less active when performing this new role. Other
roles and related skills, values, and expectations will still exist and might in
practice still be active, even if less prominent. In addition, problems can arise when
the person is unable to fulfil all expectations linked with different roles at the same
time; for example, regularly collecting data in nature may clash with family
obligations. Those potential conflicts should in the future be considered as cause
for participant dropouts or attrition as described by e.g., Eveleigh et al. [2014] or
Frensley et al. [2017].
Applying the normative-structural role concept on the meso level allows us to
identify the position and status of actors in CS at the social level and possible shifts
or transitions, as well as assumptions and expectations towards the groups
involved.
The role of the scientist in CS might change from a knowledge producer and
provider as in the deficit model [Bauer, 2016; Bucchi, 2008] to a process facilitator in
CS [Stoecker, 1999] for example in a co-created CS project (see Table 1, column 6).
Roles in the traditional science models are clearly defined and draw a boundary
between science and society. According to these models, members of the public are
not part of the research project and they are mainly defined by what they are not,
that is non-scientists. This line of argument also defines by exclusion who the
others are, in this case, the scientists: they are not the public. The public are
characterised as non-experts with knowledge deficits. Members of the public are in
the role of passive information receiver,1their status is considered low. The
researcher has the role of expert and information provider combined with a high
status [Brossard and Lewenstein, 2009; Weingart, 2001; Weingart, 2011].
Role change is linked with altered expectations towards researchers in recent
decades. For example, researchers are now expected to engage more with society,
to conduct research relevant to it, and sometimes policy [Royal Society of London,
1985; Hecker et al., 2019; Stilgoe, Lock and Wilsdon, 2014]. Fox [1982] for example
states that the scientist’s role changed from that of a notable admired by citizens to
that of a professional whose audience were mainly scholars of the same specialty in
the nineteenth century. Weingart [2011] describes a parallel development whereby
the specialisation of scientists occurred simultaneously with the popularisation of
science. This popularisation was targeted towards an audience characterised by its
curiosity on scientific facts and knowledge. In the 20th century, this attribution of
the public shifted. Now, the public was characterised by deficits and disinterest for
science. As a result, the specialisation of scientists led to the detachment of science
from society in so far as the scientist both took the role of knowledge producer and
consumer. Science became an exclusive system of knowledge production [Dickel
and Franzen, 2016].
Engaging more with society challenges researchers to take on new roles that they
might not necessarily be trained in. For example, they might choose the role of
1Although it is well acknowledged among communication scholars that reception processes
include active elements.
https://doi.org/10.22323/2.21010207 JCOM 21(01)(2022)A07 10
initiator, consultant or collaborator [Stoecker, 1999], change agent, knowledge
broker, reflective scientist who reflects processes and interactions, self-reflexive
scientist who also reflects on their own role within the collaboration and research
process, or process facilitator [Wittmayer and Schäpke, 2014] (see Table 1,
column 6). There are high demands for performing these roles that need specific
skills and capacities. In research without involving citizens, translating and
presenting one’s own scientific work and methods to an interdisciplinary or
transdisciplinary audience already is demanding [Lüthje and Thiele, 2018;
Schuck-Zöller, Brinkmann and Rödder, 2018]. CS demands even more flexibility on
the part of the scientist, as dialogue and interaction with different dialogue
partners might develop in unforeseen ways and need to be addressed accordingly
to ensure the project’s further course. Involvement in CS might challenge the
researcher’s status as an authority, and the status of volunteers might change
considerably from that of a member of society defined by deficits to that of an
acknowledged expert in a certain field.
Values, power relationships and identity
Both the micro and meso-level of communication and interaction in CS, as
categorised in columns 5 and 6 of the proposed framework, allow asking normative
questions about values and power relationships. For example: are certain activities
such as data collection by citizen scientists less valuable than analysing data or
designing the research question? Moreover, are they less appreciated and
prestigious? Do scientists in co-created projects lose power, as they may not be in
control of all steps of the research cycle? Does collaborative participation go far
enough in changing existing knowledge relations? Is changing existing structures
even the aim of the process? Does scientific knowledge lose sovereignty over
public meanings by including expertise and knowledge of members of society? Are
citizens ready to take over power and responsibility for decision-making? Does CS
want to change existing knowledge relations or does it work only as long as
existing knowledge relations are not questioned? What are policymakers’ interests
in supporting and enhancing CS? Previous research suggests that policymakers
have high expectations of CS and embrace its variety. Yet their conceptualisation of
CS does not question existing power relationships [Hecker et al., 2019]. If we detect
transitions in social roles in CS, these might be indicators “for transformative
change in the social fabric of society” [Wittmayer, Avelino et al., 2017, p. 47].
With a changing understanding of roles, questions arise about how citizens and
scientists identify themselves. Who is the scientist in CS? Who is the citizen in CS?
How do they define themselves in respect to the roles they play in CS? For
example, a person who identifies as an expert for butterfly taxonomy might
participate in an insect monitoring CS project on butterflies. Yet, their role might be
restricted to data gathering although they might be able to do the analysis as well.
Identifying oneself within the social fabric of society and specifically in the context
of CS can be important for people. It may be of great importance to individuals
whether they self-identify as still belonging to the group of scientists for example,
and determine their social identity as such, or whether this self-identity might be
challenged with the erosion of boundaries in CS. These questions are grounded in
social identity theory. Social identity is defined as a person’s sense of who they are
based on their group membership(s) [Tajfel and Turner, 2004]. Eitzel et al. [2017]
https://doi.org/10.22323/2.21010207 JCOM 21(01)(2022)A07 11
stress the point that CS participants care about how they are labelled because
terminology partly defines who they are and to which group they belong. Other
aspects related to identity in CS touch on issues of how participants are attached to
the places where they engage and what these places mean to them [Haywood,
2019; Newman, Chandler et al., 2017; Haywood, 2014].
Calling for more co-creation in CS also means reflecting on whether all actors are
well prepared and equipped for the process: whether actors are ready to question
their roles, status and identity, whether they allow for the sharing of power, and
whether they are willing to negotiate and discuss.
Discussion, conclusion and outlook
The proposed framework aims at providing a structured way to capture
communication and interaction in and about CS. This structuring is especially
important because in reality, CS projects are often far more complex and for
example aims of communication or level of involvement can change over time.
These changes need flexibility and adaption on the side of project initiators like in
the case of a project where citizens were asked to do snow observations across the
Pacific Northwest, U.S.A. [Dickerson-Lange et al., 2016]. The project engaged with
various volunteers in the beginning of the three snow seasons and gained great
outreach but sparse data. In the following years, the scientists shifted the
collaboration to an educational activity. They chose to work more closely with an
outdoor science school, which led to both better data quality and met the aim of an
educational outreach. One key factor was the alignment of the CS activities with
the regular activities and interests of science school attendants. However, the
scientists had to abandon their aim to gain broad spatial data coverage but get
higher quality data on selected sites instead [Dickerson-Lange et al., 2016]. With
the change in actors involved and considering their motivation, the aims of
communication changed, too.
In simplifying the complexity of communication and interaction in CS, the
framework provides the opportunity for researchers, science communicators, and
participants to critically reflect on their tasks, aims of communication, their
understanding of the interaction space, and their roles as actors in CS. The purpose
of the framework is not to suggest linear processes or offer one-size-fits-all
guidelines, but rather to offer the opportunity for reflection to both scholars and CS
project managers.
To limit the framework to essential variables, we have not further differentiated the
actor groups of scientists and members of the public, nor have we included other
actors that might be influential in the communication process, such as
policymakers or the media. However, the framework does offer the possibility of
integrating these actors and defining their involvement.
Future research can use the presented framework as a starting point to further
clarify communication and interaction, thus helping to inform explicit instructions
so as to enable communication between actor groups who might not share the
same knowledge background [Østerlund and Crowston, 2019].
https://doi.org/10.22323/2.21010207 JCOM 21(01)(2022)A07 12
For the further development of CS, Irwin [2015] calls for scientific, institutional and
citizenship learning, and puts emphasis on institutional learning. So far, literature
on learning in CS has focused mostly on participants’ learning. Future research
should shed light on the other partner in the research process: the scientist.
Scientists need to be aware of their own roles and their own need for learning in
and through CS, especially in communication and interaction. Questions should be
considered such as: what do scientists learn to engage with citizens to various
degrees? What is their self-understanding as scientists? Does the sense of identity
change in long-term engagement projects? Research on learning should consider
and add to both theoretical frameworks and empirical research, accompanied by an
empirical assessment of role understanding.
CS project initiators can use the proposed framework to reflect on aims and roles in
their projects. However, it should be made clear what roles they want actors to
play, and to design their project as well as their communication accordingly. We
also invite researchers to use the framework to structure empirical studies of CS
projects and their communication and interactions to develop new research
questions. Further research could critically analyse the framework by applying
other theories and concepts, such as activity theory, which might provide further
insights into communication and interaction in CS.
Acknowledgments Susanne Hecker gratefully acknowledges the support of iDiv funded by the
German Research Foundation (DFG-FZT 118, 202548816).
References Bauer, M. W. (2016). ‘Results of the essay competition on the ‘deficit concept’’.
Public Understanding of Science 25 (4), pp. 398–399.
https://doi.org/10.1177/0963662516640650.
Bergold, J. and Thomas, S. (2010). ‘Partizipative Forschung’. In: Handbuch
Qualitative Forschung in der Psychologie. Ed. by G. Mey and K. Mruck.
Wiesbaden, Germany: VS Verlag für Sozialwissenschaften, pp. 333–344.
https://doi.org/10.1007/978-3-531-92052-8_23.
Bonfadelli, H., Fähnrich, B., Lüthje, C., Milde, J., Rhomberg, M. and Schäfer, M. S.
(2017). ‘Das Forschungsfeld Wissenschaftskommunikation’. In: Forschungsfeld
Wissenschaftskommunikation. Ed. by H. Bonfadelli, B. Fähnrich, C. Lüthje,
J. Milde, M. Rhomberg and M. S. Schäfer. Wiesbaden, Germany: Springer,
pp. 3–14. https://doi.org/10.1007/978-3-658-12898-2_1.
Bonney, R. (1996). ‘Citizen science: a lab tradition’. Living Bird: for the Study and
Conservation of Birds 15 (4), pp. 7–15.
Bonney, R., Phillips, T. B., Ballard, H. L. and Enck, J. W. (2016). ‘Can citizen science
enhance public understanding of science?’ Public Understanding of Science 25 (1),
pp. 2–16. https://doi.org/10.1177/0963662515607406.
Brenton, P., von Gavel, S., Vogel, E. and Lecoq, M.-E. (2018). ‘Technology
infrastructure for citizen science’. In: Citizen science: innovation in open
science, society and policy. Ed. by S. Hecker, M. Haklay, A. Bowser, Z. Makuch,
J. Vogel and A. Bonn. London, U.K.: UCL Press, pp. 63–80.
https://doi.org/10.2307/j.ctv550cf2.12.
https://doi.org/10.22323/2.21010207 JCOM 21(01)(2022)A07 13
Brossard, D. and Lewenstein, B. V. (2009). ‘A critical appraisal of models of public
understanding of science: using practice to inform theory’. In: Communicating
science: new agendas in communication. Ed. by L. Kahlor and P. A. Stout.
1st ed. New York, NY, U.S.A.: Routledge, pp. 11–39.
https://doi.org/10.4324/9780203867631.
Brouwer, S. and Hessels, L. K. (2019). ‘Increasing research impact with citizen
science: the influence of recruitment strategies on sample diversity’. Public
Understanding of Science 28 (5), pp. 606–621.
https://doi.org/10.1177/0963662519840934.
Bucchi, M. (2008). ‘Of deficits, deviations and dialogues: theories of public
communication of science’. In: Handbook of public communication of science
and technology. Ed. by M. Bucchi and B. Trench. London, U.K.: Routledge,
pp. 57–76. https://doi.org/10.4324/9780203928240.
Burns, M. and Medvecky, F. (2018). ‘The disengaged in science communication:
how not to count audiences and publics’. Public Understanding of Science 27 (2),
pp. 118–130. https://doi.org/10.1177/0963662516678351.
Callero, P. L. (1994). ‘From role-playing to role-using: understanding role as
resource’. Social Psychology Quarterly 57 (3), pp. 228–243.
https://doi.org/10.2307/2786878.
Caplan, N. (1979). ‘The two-communities theory and knowledge utilization’.
American Behavioral Scientist 22 (3), pp. 459–470.
https://doi.org/10.1177/000276427902200308.
Crowston, K., Mitchell, E. and Østerlund, C. (2019). ‘Coordinating advanced crowd
work: extending citizen science’. Citizen Science: Theory and Practice 4 (1), 16.
https://doi.org/10.5334/cstp.166.
Dahrendorf, R. (2010). Homo Sociologicus: ein Versuch zur Geschichte, Bedeutung
und Kritik der Kategorie der sozialen Rolle. Wiesbaden, Germany: VS Verlag
für Sozialwissenschaften.
Danielsen, F., Burgess, N. D., Coronado, I., Enghoff, M., Holt, S., Jensen, P. M.,
Poulsen, M. K. and Rueda, R. M. (2018). ‘The value of indigenous and local
knowledge as citizen science’. In: Citizen science: innovation in open science,
society and policy. Ed. by S. Hecker, M. Haklay, A. Bowser, Z. Makuch, J. Vogel
and A. Bonn. London, U.K.: UCL Press, pp. 110–123.
https://doi.org/10.2307/j.ctv550cf2.15.
Davis, E., Caffrey, J. M., Coughlan, N. E., Dick, J. T. A. and Lucy, F. E. (2018).
‘Communications, outreach and citizen science: spreading the word about
invasive alien species’. Management of Biological Invasions 9 (4), pp. 515–525.
https://doi.org/10.3391/mbi.2018.9.4.14.
de Vries, M., Land-Zandstra, A. and Smeets, I. (2019). ‘Citizen scientists’
preferences for communication of scientific output: a literature review’. Citizen
Science: Theory and Practice 4 (1), 2. https://doi.org/10.5334/cstp.136.
Dickel, S. and Franzen, M. (2016). ‘The “problem of extension” revisited: new
modes of digital participation in science’. JCOM 15 (01), A06.
https://doi.org/10.22323/2.15010206.
Dickerson-Lange, S. E., Eitel, K. B., Dorsey, L., Link, T. E. and Lundquist, J. D.
(2016). ‘Challenges and successes in engaging citizen scientists to observe snow
cover: from public engagement to an educational collaboration’. JCOM 15 (01),
A01. https://doi.org/10.22323/2.15010201.
https://doi.org/10.22323/2.21010207 JCOM 21(01)(2022)A07 14
Druschke, C. G. and Seltzer, C. E. (2012). ‘Failures of engagement: lessons learned
from a citizen science pilot study’. Applied Environmental Education &
Communication 11 (3–4), pp. 178–188.
https://doi.org/10.1080/1533015X.2012.777224.
Eitzel, M. V., Cappadonna, J. L., Santos-Lang, C., Duerr, R. E., Virapongse, A.,
West, S. E., Kyba, C. C. M., Bowser, A., Cooper, C. B., Sforzi, A., Metcalfe, A. N.,
Harris, E. S., Thiel, M., Haklay, M., Ponciano, L., Roche, J., Ceccaroni, L.,
Shilling, F. M., Dörler, D., Heigl, F., Kiessling, T., Davis, B. Y. and Jiang, Q.
(2017). ‘Citizen science terminology matters: exploring key terms’. Citizen
Science: Theory and Practice 2 (1), 1. https://doi.org/10.5334/cstp.96.
Eveleigh, A., Jennett, C., Blandford, A., Brohan, P. and Cox, A. L. (2014). ‘Designing
for dabblers and deterring drop-outs in citizen science’. In: CHI ’14: Proceedings
of the SIGCHI Conference on Human Factors in Computing Systems. New York, NY,
U.S.A.: ACM Press, pp. 2985–2994.
https://doi.org/10.1145/2556288.2557262.
Fox, R. (1982). ‘The scientist and his public in nineteenth-century France’. Social
Science Information 21 (4–5), pp. 697–718.
https://doi.org/10.1177/053901882021004012.
Frensley, T., Crall, A., Stern, M., Jordan, R., Gray, S., Prysby, M., Newman, G.,
Hmelo-Silver, C., Mellor, D. and Huang, J. (2017). ‘Bridging the benefits of
online and community supported citizen science: a case study on motivation
and retention with conservation-oriented volunteers’. Citizen Science: Theory and
Practice 2 (1), 4. https://doi.org/10.5334/cstp.84.
Geoghegan, H., Dyke, A., Pateman, R., West, S. and Everett, G. (2016).
Understanding motivations for citizen science. Final report on behalf of UKEOF,
University of Reading, Stockholm Environment Institute (University of York)
and University of the West of England. Swindon, U.K.: UKEOF.
URL:http://www.ukeof.org.uk/resources/citizen-science-resources/Mot
ivationsforCSREPORTFINALMay2016.pdf.
Greenwood, D. J., Whyte, W. F. and Harkavy, I. (1993). ‘Participatory action
research as a process and as a goal’. Human Relations 46 (2), pp. 175–192.
https://doi.org/10.1177/001872679304600203.
Haklay, M. (2013). ‘Citizen science and volunteered geographic information:
overview and typology of participation’. In: Crowdsourcing geographic
knowledge: Volunteered Geographic Information (VGI) in theory and practice.
Ed. by D. Sui, S. Elwood and M. Goodchild. Dordrecht, Netherlands: Springer,
pp. 105–122. https://doi.org/10.1007/978-94-007-4587-2_7.
— (2015). Citizen science and policy: a European perspective. Washington, DC,
U.S.A.: Commons Lab, Woodrow Wilson International Center for Scholars.
URL:https://www.wilsoncenter.org/publication/citizen-science-and-po
licy-european-perspective.
— (2018). ‘Participatory citizen science’. In: Citizen science: innovation in open
science, society and policy. Ed. by S. Hecker, M. Haklay, A. Bowser, Z. Makuch,
J. Vogel and A. Bonn. London, U.K.: UCL Press, pp. 52–62.
https://doi.org/10.2307/j.ctv550cf2.11.
Haklay, M. and Eleta, I. (2019). ‘On the front line of community-led air quality
monitoring’. In: Integrating human health into urban and transport planning:
a framework. Ed. by M. Nieuwenhuijsen and H. Khreis. Cham, Switzerland:
Springer, pp. 563–580. https://doi.org/10.1007/978-3-319-74983-9_27.
https://doi.org/10.22323/2.21010207 JCOM 21(01)(2022)A07 15
Haywood, B. (2014). ‘A “sense of place” in public participation in scientific
research’. Science Education 98 (1), pp. 64–83.
https://doi.org/10.1002/sce.21087.
— (2019). ‘Citizen science as a catalyst for place meaning and attachment’.
Environment, Space, Place 11 (1), pp. 126–151.
https://doi.org/10.5749/envispacplac.11.1.0126.
Hecker, S., Wicke, N., Haklay, M. and Bonn, A. (2019). ‘How does policy
conceptualise citizen science? A qualitative content analysis of international
policy documents’. Citizen Science: Theory and Practice 4 (1), 32.
https://doi.org/10.5334/cstp.230.
Hobbs, S. J. and White, P. C. L. (2012). ‘Motivations and barriers in relation to
community participation in biodiversity recording’. Journal for Nature
Conservation 20 (6), pp. 364–373.
https://doi.org/10.1016/j.jnc.2012.08.002.
Hoover, E. (2016). ‘“We’re not going to be guinea pigs;” citizen science and
environmental health in a Native American community’. JCOM 15 (01), A05.
https://doi.org/10.22323/2.15010205.
Irwin, A. (1995). Citizen science: a study of people, expertise and sustainable
development. London, U.K.: Routledge.
https://doi.org/10.4324/9780203202395.
— (2015). ‘Citizen science and scientific citizenship: same words, different
meanings?’ In: Science communication today: current strategies and means of
action. Ed. by B. Schiele, J. Le Marec and P. Baranger. Nancy, France: Presses
Universitaires de Nancy, pp. 29–38.
Klose, S. (2019). ‘The emergence and evolution of an external actor’s regional role:
an interactionist role theory perspective’. Cooperation and Conflict 54 (3),
pp. 426–441. https://doi.org/10.1177/0010836718774584.
Krappmann, L. (1971). Soziologische Dimensionen der Identität: strukturelle
Bedingungen für die Teilnahme an Interaktionsprozessen. Stuttgart, Germany:
Klett-Cotta.
Kullenberg, C. and Kasperowski, D. (2016). ‘What is citizen science?
A scientometric meta-analysis’. PLoS ONE 11 (1), e0147152.
https://doi.org/10.1371/journal.pone.0147152.
Land-Zandstra, A. M., Devilee, J. L. A., Snik, F., Buurmeijer, F. and van den
Broek, J. M. (2016). ‘Citizen science on a smartphone: participants’ motivations
and learning’. Public Understanding of Science 25 (1), pp. 45–60.
https://doi.org/10.1177/0963662515602406.
Lewenstein, B. V. (2016). ‘Can we understand citizen science?’ JCOM 15 (01), E.
https://doi.org/10.22323/2.15010501.
Liboiron, M. (2019). ‘The power (relations) of citizen science’. In: CitSci2019
(Raleigh, NC, U.S.A. 13th–17th March 2019).
Linton, R. (1936). The study of man: an introduction. New York, NY, U.S.A.:
Appleton-Century.
Loorbach, D. (2010). ‘Transition management for sustainable development:
a prescriptive, complexity-based governance framework’. Governance 23 (1),
pp. 161–183. https://doi.org/10.1111/j.1468-0491.2009.01471.x.
Loosen, W. and Schmidt, J.-H. (2012). ‘(Re-)discovering the audience’. Information,
Communication & Society 15 (6), pp. 867–887.
https://doi.org/10.1080/1369118x.2012.665467.
https://doi.org/10.22323/2.21010207 JCOM 21(01)(2022)A07 16
Lüthje, C. and Thiele, F. (2018). ‘The communicative construction of
interdisciplinarity: group discussions with climate scientists’. GAIA —
Ecological Perspectives for Science and Society 27 (3), pp. 306–311.
https://doi.org/10.14512/gaia.27.3.11.
Lynch, K. D. (2007). ‘Modeling role enactment: linking role theory and social
cognition’. Journal for the Theory of Social Behaviour 37 (4), pp. 379–399.
https://doi.org/10.1111/j.1468-5914.2007.00349.x.
Mazumdar, S., Ceccaroni, L., Piera, J., Hölker, F., Berre, A. J., Arlinghaus, R. and
Bowser, A. (2018). ‘Citizen science technologies and new opportunities for
participation’. In: Citizen science: innovation in open science, society and
policy. Ed. by S. Hecker, M. Haklay, A. Bowser, Z. Makuch, J. Vogel and
A. Bonn. London, U.K.: UCL Press, pp. 303–320.
https://doi.org/10.2307/j.ctv550cf2.28.
Mead, G. H. (1934). Mind, self and society. Chicago, IL, U.S.A.: University of
Chicago Press.
Merton, R. K. (1957). ‘The role-set: problems in sociological theory’. The British
Journal of Sociology 8 (2), pp. 106–120. https://doi.org/10.2307/587363.
Metcalfe, J. (2019). ‘Comparing science communication theory with practice:
an assessment and critique using Australian data’. Public Understanding of
Science 28 (4), pp. 382–400. https://doi.org/10.1177/0963662518821022.
Meyer, G. and Sandøe, P. (2012). ‘Going public: good scientific conduct’. Science and
Engineering Ethics 18 (2), pp. 173–197.
https://doi.org/10.1007/s11948-010-9247-x.
Miebach, B. (2010). Soziologische Handlungstheorie: eine Einführung. Wiesbaden,
Germany: VS Verlag für Sozialwissenschaften.
https://doi.org/10.1007/978-3-531-92185-3.
Neuberger, C., Bartsch, A., Reinemann, C., Fröhlich, R., Hanitzsch, T. and
Schindler, J. (2019). ‘Der digitale Wandel der Wissensordnung. Theorierahmen
für die Analyse von Wahrheit, Wissen und Rationalität in der öffentlichen
Kommunikation’. M&K Medien & Kommunikationswissenschaft 67 (2),
pp. 167–186. https://doi.org/10.5771/1615-634X-2019-2-167.
Newman, G., Chandler, M., Clyde, M., McGreavy, B., Haklay, M., Ballard, H.,
Gray, S., Scarpino, R., Hauptfeld, R., Mellor, D. and Gallo, J. (2017). ‘Leveraging
the power of place in citizen science for effective conservation decision
making’. Biological Conservation 208, pp. 55–64.
https://doi.org/10.1016/j.biocon.2016.07.019.
Newman, G., Wiggins, A., Crall, A., Graham, E., Newman, S. and Crowston, K.
(2012). ‘The future of citizen science: emerging technologies and shifting
paradigms’. Frontiers in Ecology and the Environment 10 (6), pp. 298–304.
https://doi.org/10.1890/110294.
Østerlund, C. and Crowston, K. (2019). ‘Documentation and access to knowledge
in online communities: know your audience and write appropriately?’ Journal of
the Association for Information Science and Technology 70 (6), pp. 619–633.
https://doi.org/10.1002/asi.24152.
Pandya, R. (2018). ‘Preface’. In: Learning through citizen science: enhancing
opportunities by design. Ed. by R. Pandya and K. A. Dibner. Washington, DC,
U.S.A.: The National Academies Press, pp. vii–ix.
https://doi.org/10.17226/25183.
https://doi.org/10.22323/2.21010207 JCOM 21(01)(2022)A07 17
Ramirez-Andreotta, M. D., Brusseau, M. L., Artiola, J. F., Maier, R. M. and
Gandolfi, A. J. (2015). ‘Building a co-created citizen science program with
gardeners neighboring a superfund site: the Gardenroots case study’.
International Public Health Journal 7 (1), 13.
Riesch, H., Potter, C. and Davies, L. (2013). ‘Combining citizen science and public
engagement: the Open AirLaboratories Programme’. JCOM 12 (03), A03.
https://doi.org/10.22323/2.12030203.
Rotman, D., Hammock, J., Preece, J., Hansen, D., Boston, C., Bowser, A. and He, Y.
(2014). ‘Motivations affecting initial and long-term participation in citizen
science projects in three countries’. In: Proceedings of iConference 2014. iSchools,
pp. 110–124. https://doi.org/10.9776/14054.
Roy, H. E., Pocock, M. J. O., Preston, C. D., Roy, D. B., Savage, J., Tweddle, J. C. and
Robinson, L. D. (2012). Understanding citizen science and environmental
monitoring. Final report on behalf of UK-EOF. NERC Centre for Ecology &
Hydrology and Natural History Museum.
Royal Society of London (1985). The public understanding of science. London, U.K.:
The Royal Society of London. URL:https://royalsociety.org/topics-policy
/publications/1985/public-understanding-science/.
Salmon, R. A., Rammell, S., Emeny, M. T. and Hartley, S. (2021). ‘Citizens, scientists,
and enablers: a tripartite model for citizen science projects’. Diversity 13 (7), 309.
https://doi.org/10.3390/d13070309.
Scheliga, K., Friesike, S., Puschmann, C. and Fecher, B. (2018). ‘Setting up crowd
science projects’. Public Understanding of Science 27 (5), pp. 515–534.
https://doi.org/10.1177/0963662516678514.
Schuck-Zöller, S., Brinkmann, C. and Rödder, S. (2018). ‘Integrating research and
practice in emerging climate services — Lessons from other transdisciplinary
dialogues’. In: Communicating climate change information for
decision-making. Ed. by S. Serrao-Neumann, A. Coudrain and L. Coulter.
Cham, Switzerland: Springer, pp. 105–118.
https://doi.org/10.1007/978-3-319-74669-2_8.
Shirk, J. L., Ballard, H. L., Wilderman, C. C., Phillips, T., Wiggins, A., Jordan, R.,
McCallie, E., Minarchek, M., Lewenstein, B. V., Krasny, M. E. and Bonney, R.
(2012). ‘Public participation in scientific research: a framework for deliberate
design’. Ecology and Society 17 (2), 29.
https://doi.org/10.5751/ES-04705-170229.
Simpson, B. and Carroll, B. (2008). ‘Re-viewing ‘role’ in processes of identity
construction’. Organization 15 (1), pp. 29–50.
https://doi.org/10.1177/1350508407084484.
Sinwell, L. (2012). ‘Transformative left-wing parties’ and grassroots organizations:
unpacking the politics of “top-down” and “bottom-up” development’. Geoforum
43 (2), pp. 190–198. https://doi.org/10.1016/j.geoforum.2011.10.008.
Stepenuck, K. F. and Genskow, K. D. (2018). ‘Characterizing the breadth and depth
of volunteer water monitoring programs in the United States’. Environmental
Management 61 (1), pp. 46–57. https://doi.org/10.1007/s00267-017-0956-7.
Stilgoe, J., Lock, S. J. and Wilsdon, J. (2014). ‘Why should we promote public
engagement with science?’ Public Understanding of Science 23 (1), pp. 4–15.
https://doi.org/10.1177/0963662513518154.
Stoecker, R. (1999). ‘Are academics irrelevant? Roles for scholars in participatory
research’. American Behavioral Scientist 42 (5), pp. 840–854.
https://doi.org/10.1177/00027649921954561.
https://doi.org/10.22323/2.21010207 JCOM 21(01)(2022)A07 18
Tajfel, H. and Turner, J. (2004). ‘An integrative theory of intergroup conflict’. In:
Organizational identity: a reader. Ed. by M. J. Hatch and M. Schultz. Oxford,
U.K.: Oxford University Press, pp. 56–65.
Turner, R. H. (1985). ‘Unanswered questions in the convergence between
structuralist and interactionist role theories’. In: Perspectives on sociological
theory. Volume 2: Micro-sociological theory. Ed. by H. J. Helle and
S. N. Eisenstadt. Beverly Hills and London: SAGE Publications, pp. 22–36.
Turnhout, E., Stuiver, M., Klostermann, J., Harms, B. and Leeuwis, C. (2013). ‘New
roles of science in society: different repertoires of knowledge brokering’. Science
and Public Policy 40 (3), pp. 354–365. https://doi.org/10.1093/scipol/scs114.
Tweddle, J. C., Robinson, L. D., Pocock, M. J. O. and Roy, H. E. (2012). Guide to
citizen science: developing, implementing and evaluating citizen science to study
biodiversity and the environment in the UK. Natural History Museum and NERC
Centre for Ecology & Hydrology for UK-EOF.
van der Horst, M. (2016). ‘Role theory’. Sociology — Oxford Bibliographies.
https://doi.org/10.1093/obo/9780199756384-0175.
van der Sanden, M. C. A. and Meijman, F. J. (2008). ‘Dialogue guides awareness
and understanding of science: an essay on different goals of dialogue leading to
different science communication approaches’. Public Understanding of Science 17
(1), pp. 89–103. https://doi.org/10.1177/0963662506067376.
von Unger, H. (2014). Partizipative Forschung: Einführung in die
Forschungspraxis. Wiesbaden, Germany: Springer VS.
https://doi.org/10.1007/978-3-658-01290-8.
Wagenknecht, K., Woods, T., Nold, C., Rüfenacht, S., Voigt-Heucke, S., Caplan, A.,
Hecker, S. and Vohland, K. (2021). ‘A question of dialogue? Reflections on how
citizen science can enhance communication between science and society’. JCOM
20 (03), A13. https://doi.org/10.22323/2.20030213.
Weingart, P. (2001). Die Stunde der Wahrheit? Zum Verhältnis der Wissenschaft zu
Politik, Wirtschaft und Medien in der Wissensgesellschaft. 1st ed. Weilerswist,
Germany: Velbrück Wissenschaft.
— (2011). ‘Die Wissenschaft der Öffentlichkeit und die Öffentlichkeit der
Wissenschaft’. In: Wissenschaft und Hochschulbildung im Kontext von
Wirtschaft und Medien. Ed. by B. Hölscher and J. Suchanek. Wiesbaden,
Germany: VS Verlag für Sozialwissenschaften, pp. 45–61.
https://doi.org/10.1007/978-3-531-92648-3_4.
Wicks, P. G. and Reason, P. (2009). ‘Initiating action research: challenges and
paradoxes of opening communicative space’. Action Research 7 (3), pp. 243–262.
https://doi.org/10.1177/1476750309336715.
Wiggins, A. and Crowston, K. (2015). ‘Surveying the citizen science landscape’.
First Monday 20 (1–5). https://doi.org/10.5210/fm.v20i1.5520.
Wittmayer, J. M., Avelino, F., van Steenbergen, F. and Loorbach, D. (2017). ‘Actor
roles in transition: insights from sociological perspectives’. Environmental
Innovation and Societal Transitions 24, pp. 45–56.
https://doi.org/10.1016/j.eist.2016.10.003.
Wittmayer, J. M. and Schäpke, N. (2014). ‘Action, research and participation: roles
of researchers in sustainability transitions’. Sustainability Science 9 (4),
pp. 483–496. https://doi.org/10.1007/s11625-014-0258-4.
Yodanis, C. L. (2003). ‘Role theory’. In: International encyclopedia of marriage and
family. Ed. by J. J. Ponzetti. New York, NY, U.S.A.: Macmillan Reference.
https://doi.org/10.22323/2.21010207 JCOM 21(01)(2022)A07 19
Authors Susanne Hecker researches citizen science at the interface between science, society
and politics and the role of communication in participative research projects. She
has been instrumental in building the international citizen science network and is
currentyl First Chair of the European Citizen Science Association (ECSA). She is
first editor of the book “Citizen Science — Innovation in Open Science, Society and
Policy” published by UCL Press in 2018. Susanne received her Ph.D. from the
Technische Universität Braunschweig in communication science.
Museum für Naturkunde Leibniz Institute for Evolution and Biodiversity Science,
Invalidenstraße 43, 10115 Berlin, Germany; Helmholtz Centre for Environmental
Research — UFZ, Department of Ecosystem Services, Permoserstr. 15,
04318 Leipzig, Germany; German Centre for Integrative Biodiversity Research
(iDiv) Halle-Jena-Leipzig, Puschstr. 4, 04103 Leipzig, Germany.
E-mail: susanne.hecker@mfn.berlin.
Monika Taddicken is a professor of Communication and Media Sciences at the
Technische Universität Braunschweig, Germany. She received her Ph.D. in
communication research from the University of Hohenheim, Germany. She is
currently working on the audience’s perspective of science communication. She
has also published several papers on computer-mediated communication, and
survey methodology.
Institute for Communication Science, TU Braunschweig, Bienroder Weg 97,
38106 Braunschweig, Germany. E-mail: m.taddicken@tu-braunschweig.de.
Hecker, S. and Taddicken, M. (2022). ‘Deconstructing citizen science: a frameworkHow to cite
on communication and interaction using the concept of roles’. JCOM 21 (01), A07.
https://doi.org/10.22323/2.21010207.
c
The Author(s). This article is licensed under the terms of the Creative Commons
Attribution — NonCommercial — NoDerivativeWorks 4.0 License.
ISSN 1824-2049. Published by SISSA Medialab. jcom.sissa.it
https://doi.org/10.22323/2.21010207 JCOM 21(01)(2022)A07 20
