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Structure and development of science communication research: Co-citation analysis of a developing field

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Since the early 1990s, there has been a considerable increase in the number of scientific studies on science communication, and this increase has been accompanied by a diversification of the research field. This study focuses on one aspect of this development: it analyses how citation network structures within the field have developed over time, and whether science communication research shows signs of becoming a research field or a discipline in its own right. Employing a co-citation analysis of scholarly publications published between 1996 and 2015, it assesses to what extent a coherent communication network exists within science communication research. The results show a field with a diverse internal structure and clear internal changes over time which suggest an increasing emancipation of the field.
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Structure and development of science communication
research: co-citation analysis of a developing field
Adrian Rauchfleisch and Mike S. Schäfer
Since the early 1990s, there has been a considerable increase in the
number of scientific studies on science communication, and this increase
has been accompanied by a diversification of the research field. This study
focuses on one aspect of this development: it analyses how citation
network structures within the field have developed over time, and whether
science communication research shows signs of becoming a research field
or a discipline in its own right. Employing a co-citation analysis of scholarly
publications published between 1996 and 2015, it assesses to what extent
a coherent communication network exists within science communication
research. The results show a field with a diverse internal structure and
clear internal changes over time which suggest an increasing emancipation
of the field.
Abstract
Scholarly communication; Science communication: theory and modelsKeywords
https://doi.org/10.22323/2.17030207DOI
Along with the rising importance of science communication itself, the scientific
community has started to analyze the characteristics, antecedents and effects of
science communication. Research interest emerged in the 1990s from disciplinary
fields like sociology [e.g. Bucchi, 1998; Peters, 1994; Weingart, 1998], media and
communication science [e.g. Dunwoody, 1980; Goodell, 1977; Ruhrmann, 1992], or
political science [e.g. Miller, 1991; Miller, 1998], as well as from interdisciplinary
fields like the “science and technology studies” [e.g. Callon, 1995; Lewenstein,
1995], among others. Since then, there has been a considerable increase in the
number of scientific studies on science communication [Guenther and Joubert,
2017; Schäfer, 2012]. In addition, and even though most authors of scientific
publications in the field are still based in the U.S. [Guenther and Joubert, 2017, 10f],
the objects of analysis have become more diverse, moving away from a strong early
focus on the Anglophone world and on national, quality print media; and research
designs and methods have diversified as well [Schäfer, 2011; Schäfer and
Schlichting, 2014].
Along with this quantitative growth and diversification, a stronger
institutionalization of the field and an increasing emancipation from mother
disciplines and neighboring fields have been diagnosed by some scholars [for a
differing position see Trench and Bucchi, 2010]. They have argued that research on
Article Journal of Science Communication 17(03)(2018)A07 1
science communication has become “an independent academic field, different from
both science and technology studies and communication and media theory”
[Gascoigne et al., 2010, p. 1; cf. Bauer, 2009; Delfanti, 2008]. It has even been
cautiously debated in JCOM — Journal of Science Communication whether the
field “has achieved the status of a discipline” [Gascoigne et al., 2010, p. 5].
This study focuses on one aspect of this development: it analyses how citation
network structures within the research field have developed over time. Employing
a co-citation analysis of scholarly publications, it assesses to what extent a coherent
“communication network” [Gascoigne et al., 2010, p. 4] exists within the field, or to
what extent different networks continue to co-exist. In doing so, it analyzes a) how
homogenous the field is, i.e. if and how many subcommunities with their own
citation structures can be found, b) how they have developed over time and c) how
strong the linkages between those subcommunities are. Answering these questions
allows us to assess if science communication research has indeed become
institutionalized, at least with regards to the formal communication of scholars in
this field, and if so, when and how it has differentiated itself from other fields or
disciplines.
Conceptual
framework:
communicative
networks as core
characteristics of
research fields
“Discipline” and “research field” are commonly-used terms in the history and
philosophy of science. Nonetheless, it is difficult to define these concepts precisely,
as “[t]here is considerable ambiguity in the concept” [Sugimoto and Weingart,
2015, p. 775]. Understandings of disciplines, for example, have been developed in
different ways, based on different criteria, and have, consequently, come to
different descriptions [for an overview see Sugimoto and Weingart, 2015,
pp. 778ff.]. Some are based on theoretically developed, deductive
conceptualizations which organize disciplines according to their “cognitive
coherence” [Bulick, 1982, p. 12], to the existence of a social community of scholars
[e.g. Lattuca, 2002], the existence of a specific language [e.g. Krishnan, 2009] or
communicative network [Sugimoto and Weingart, 2015]. Other understandings are
based on disciplinary narratives emphasizing founding figures or influential
publications, crucial conferences or pressing social needs that led to the
establishing of certain disciplines [Sugimoto and Weingart, 2015]. In addition, there
are — partly idiosyncratic — conceptualizations of disciplines that have been
developed, implicitly or explicitly, around certain measurements, i.e. around
publication- or author-based metrics or around certain languages, topics or ideas
which are used to distinguish disciplines [Sugimoto and Weingart, 2015].
As a result, diverse understandings of disciplinarity exist, which poses a difficulty
for an empirical study. This difficulty can be remedied, however, with two
conceptual specifications: firstly, scholars like Good [2000] argue that
“disciplinarity is not a yes or no proposition” (p. 260), and that disciplines have to
be understood as “ever-changing frameworks within which scientific activity is
organized” [Good, 2000, p. 260]. Therefore, the boundaries between a research field
and a discipline are fluid, and come down to the degree to which definitorial core
characteristics of disciplines are fulfilled.
Secondly, scholars have acknowledged that the number of such core characteristics,
which are repeatedly described in scholarly literature, is limited. “[T]here are a few
common axes upon which conceptualizations, disciplinary narratives, and
https://doi.org/10.22323/2.17030207 JCOM 17(03)(2018)A07 2
measurements revolve” [Sugimoto and Weingart, 2015, p. 775]. Three such ‘axes’
seem to be most common in the respective literature [see Gascoigne et al., 2010;
Schützenmeister, 2008]: firstly, research fields and disciplines are distinguished
along institutional characteristics, i.e. according to whether they have their own
conferences, professional associations, outlets for publication etc. Secondly, they
are delineated along epistemic characteristics, i.e. according to whether they have a
common object, epistemic history and joint modes of scholarly inquiry. Thirdly,
research fields and disciplines are often distinguished based on social and
communicative characteristics.
The first two of these axes have been repeatedly described with regards
to research on science communication. In the case of its institutional characteristics,
scholars have portrayed it as an increasingly institutionalized research field, relying
on a broad range of indicators. Firstly, they have pointed towards established
professional associations and their conferences. They emphasize the international
Public Communication of Science and Technology (PCST) Network, for example,
which was founded in the 1980s, “and held its first international conference in 1989.
Since then it has held 16 formal international conferences and one symposium
in cities from Madrid and Montreal to Cape Town and Seoul. Recent conferences
attracted 500–700 registrants from 50 different countries” [Gascoigne et al., 2010,
p. 2]. They also emphasized other associations and meetings, like the “American
Association for the Advancement of Science (AAAS) [which] has a strong
tradition of science communication”, the Association of British Science Writers,
or the European Science Events Association with “89 member organisations from
34 countries” [Gascoigne et al., 2010, p. 2]. Secondly, they have pointed towards the
journals which have become established in the field, such as “Public Understanding
of Science”, “Science Communication” or “JCOM — Journal of Science
Communication” [e.g. Gascoigne et al., 2010, p. 3], and the various introductory and
handbooks which have been published [e.g. Bauer and Bucchi, 2007; Fähnrich et al.,
2018; Bucchi and Trench, 2014; Bonfadelli et al., 2018; Fischhoff and Scheufele, 2013;
Gregory and Miller, 1998; Hall Jamieson, Kahan and Scheufele, 2017; Nisbet et al.,
2017]. In addition, in many countries, assessments of adult scientific literacy have
been established which regularly evaluate science knowledge, comprehension and
attitudes [cf. Bauer, 2009]. Thirdly, they have described how science communication
has been established in higher education, e.g. in chairs at universities
in different countries, in “formal training at college and university levels [which]
began to take shape from 1980”, with the U.S. Directory of Science Communication
Courses and Programs listing “51 courses and programs at 44 separate
universities” or the The European Guide to Science Journalism including courses
in 27 countries, and many more courses being “offered throughout the world”
[Gascoigne et al., 2010, p. 3]. Overall, with regards to institutional characteristics,
a number of scholarly observations show a rather clear trend towards
an established, autonomous field devoted to science communication research.
Regarding epistemic characteristics, scholars have described the emergence and
development of core concepts, and of related measurements as a central
characteristic of research fields and disciplines. Even though the field is
interdisciplinary in its origin and in its approach to many of its questions, it has
been argued that it has developed a portfolio of specific concepts and
measurements [Gascoigne et al., 2010]. Concepts and models specific to the field
have been developed, like the early “public understanding of science” approach or
https://doi.org/10.22323/2.17030207 JCOM 17(03)(2018)A07 3
“deficit model” [e.g. Bauer, 2009],1or the “public engagement with science and
technology” approach [e.g. Bucchi, 2008; Schäfer, 2009]. In addition [and even
though some of the older concepts are difficult to get rid of in public debate, see the
current issue of PUS on “deficit model” Bauer, 2016], an elaboration of these
concepts is visible over time — with the field, e.g., moving from “public
understanding of science” and “deficit model” approaches to more elaborate
“Science in Society” approaches like “public engagement with science and
technology” which take the two-way, dialogical nature of communication and an
active audience into account [e.g. Bauer, Allum and Miller, 2007; Gregory and
Miller, 1998; Schäfer, 2009], or with the field elaborating on earlier approaches of
“scientific literacy” [Miller, 1983] in terms of dimensionality [e.g. Miller, 1998],
measurement [e.g. Pardo and Calvo, 2002] and concept [Kahan, 2016]. The
available publications show that, while models and concepts have been (and still
are often) imported into the field, they have been adapted. Nowadays, a number of
approaches, theories and models exist which are specific to science
communication research.
In contrast to the institutional and epistemic characteristics of science
communication research, their communicative networks have not yet been
sufficiently scrutinized. While “[c]ommunication is recognized as a crucial
component of disciplinarity, thus reinforcing the use of formal scholarly
communication in measurements of disciplinarity” [Sugimoto and Weingart, 2015,
p. 787], the analysis of science communication research exhibits considerable
shortcomings in this respect. Analyses of communication in this field have either
focused on the extent of institutionalized communication on conferences as
measured in participants or presentations [e.g. Gascoigne et al., 2010], or they have
analyzed scholarly publications without looking for network structures [e.g.
Guenther and Joubert, 2017; Gurabardhi, Gutteling and Kuttschreuter, 2004;
Schäfer, 2012]. These analyses have shown, for example, that the number of
publications has increased [Schäfer, 2012], with a “steady stream of books on
science communication reach[ing] the market every year”, the field having a
“strong record of articles”, and the respective conferences having hundreds of
papers and participants [Gascoigne et al., 2010, p. 3]. Scholars have not gone
beyond literature reviews which, however, rarely focus on the communicative
networks existing in the field [e.g. Gregory and Miller, 1998; Bucchi and Trench,
2014], even though written scholarly communication offers a way to do so, and
even though a history of bibliometric analysis exists which provides tools to
analyze these networks [for an overview see Diodato and Gellatly, 2013].
Objectives of the
study
To address this gap in scholarship, we provide a bibliometric analysis of
publications from studies of science communication. Using co-citation analysis, we
assess the communicative networks within the field as one of the crucial facets of
the development of research fields. This approach helps us to identify specific
subgroups in the field. A co-citation analysis counts how often two “items of earlier
literature are cited together by the later literature” [Small, 1973, p. 265]. Of course,
this purely quantitative criterion is only one possible approach to measure
“disciplinarity” and there are other, more qualitative criteria that Sugimoto and
1Those concepts partly also rested on conceptual foundations taken from other fields. The deficit
model, for example, shows parallels to Freire’s “banking” model in educational science [Freire, 1985].
https://doi.org/10.22323/2.17030207 JCOM 17(03)(2018)A07 4
Weingart [2015] propose. Still, “co-citation analysis is useful for rendering the
inertia of fields” [White and McCain, 1998, p. 342].
This general aim translates into three research objectives: firstly, we will analyze
which co-citation-based subcommunities exist within the field, and how they can be
characterized. Secondly, we will describe their development over time in order to assess
whether developments towards an increasingly emancipated field or even a
discipline are visible. To assess this, absolute measures of interconnectedness
within scholarly communities are difficult to use, as it is difficult to determine what
extent of homogeneity is needed for a field to be deemed a discipline — but relative
measures, such as comparisons over time, enable observers to identify trends.
Thirdly, we will assess the interconnections between the different subcommunities.
Data and methods Co-citation analysis was used to explore the development of scholarship on science
communication between 1996 and 2015. This method is mainly applied in
bibliometrics [Moed, 2005] to measure researcher performance [Nightingale and
Marshall, 2012], or to analyze the development of concepts such as Corporate
Social Responsibility over time [De Bakker, Groenewegen and Den Hond, 2005].
Such studies typically base their samples on specific (sets of) journals representing
a discipline or research field of interest.
Generally, several databases can be used for citation analyses. We chose the Scopus
database. On the one hand, it covers a number of non-English journals as well as
edited volumes and some books which are not included in the Social Science
Citation Index, providing a better foundation for the analyses of a (potentially)
international research field that is largely based in the social sciences [Gasparyan,
Ayvazyan and Kitas, 2013]. On the other hand, it is less inclusive than Google
Scholar which also includes graduate and undergraduate theses as well as
conference presentations and, thus, ensures a better coverage of the research
field itself.
To acquire an initial set of publications as a starting point for our analysis, we used
the keywords ‘science communication’ or ‘scientific communication’ in Scopus’
internal search tool. The condition for inclusion in the initial sample was that at
least one of the search terms had to appear either in the title, abstract or key words
of a publication (with non-English articles on Scopus usually having an English
abstract, enabling us to search them as well). Before proceeding with our analysis,
we checked for potential biases caused by the inclusion of the keyword “scientific
communication”. Overall, this keyword yielded only few additional publications:
only 328 publications were included in the analysis that included “scientific
communication” exclusively, and all other publications included “science
communication” as well. In addition, the most frequently quoted literature in these
publications is the same as in the rest of the sample [e.g. Latour, 1987; Kuhn, 1962;
Merton, 1973], which shows that at least in terms of citations there is a connection
between the inward-oriented “scientific communication” and the more
outward-oriented “science communication”.
As Scopus does not fully cover scientific publications before 1996 [Gasparyan,
Ayvazyan and Kitas, 2013], we only included publications from 1996 until 2015.
https://doi.org/10.22323/2.17030207 JCOM 17(03)(2018)A07 5
Based on these criteria, a cleansed corpus consisting of 2395 scholarly publications
could be identified, with full bibliographic information and abstracts available.2
This wide selection of publications was further reduced at a later stage based on
the results of the citation networks.
To prepare the data for co-citation analysis [Small, 1973], we first extracted all
references from each publication and created bibliographic couples for each
combination of references. These bibliographic couples were then merged and
further used as an undirected edge table for a network analysis with the R-package
igraph [Csárdi and Nepusz, 2006]. For the final analysis, only publications that
were cited in at least three documents were included in the co-citation analysis.3
With the edge list, an undirected network consisting of 1815 nodes (unique
references) and 53,294 edges (bibliographic couples) was created. In a next step, we
further reduced the network and considered only edges with a minimum weight of
two. Based on this reduced network with only 7703 edges, different communities
could be identified through modularity-based community detection. One of the
most efficient and well established methods is the Louvain-Algorithm [Blondel
et al., 2008]. Instead of using similarity measures, this approach relies solely on the
topology of the network and can incorporate the weights of the edges [Wallace,
Gingras and Duhon, 2009]. Each unique reference can be assigned to a specific
community. We only used communities with at least 20 unique references for
further analysis. Our final analysis included 1238 unique references and 1051 of our
original documents. The strong reduction is not surprising, because we were
selecting our data only based on two general search terms.
In a next step, the original 1051 documents were classified based on the references
they used. If the majority of the quoted references in a document belongs to a
specific community, the document was classified as being part of this community.
In the penultimate step, we identified research topics in the abstracts of the
documents using automated content analysis [Latent Dirichlet Allocation (LDA):
Blei, Ng and Jordan, 2003]. This method is particularly useful in studying scientific
topics as the seminal study of Griffiths and Steyvers [Griffiths and Steyvers, 2004]
has demonstrated. Abstracts usually include all the relevant topics of an academic
publication. We conducted the LDA with the program MALLET [McCallum, 2002],
within R.4Parameters for the LDA were chosen based on Steyvers and Griffiths
[2007]. Asymmetric dirichlet priors over the document — topic distributions were
2For articles and book chapters with up to three authors, the publication year and page numbers
were included in the id. With this step, it was possible to distinguish multiple publications by the
same author(s) in the same year.
3Researchers usually use such thresholds in co-citation analyses. In the past, these thresholds were
often very high in order to reduce the amount of data, as computational power was a limiting factor.
With these limitations being less salient nowadays, we can conduct citation analyses with
considerably larger data sets. Still, however, setting a threshold is useful to receive more refined
solutions. Accordingly, we decided to use a threshold as low as possible that still provides a clear
solution [see also Klavans and Boyack, 2017; Trujillo and Long, 2018]. The threshold of three used
here means that a bibliographic title had to be quoted in at least 1.2% of our 2.395 publications.
4Before running the model stop words were filtered based on the list available in MALLET
including the words we used as search terms (scientific, science and communication), all numbers
were deleted, hyphens were replaced with space characters and all words were converted to
lower-case.
https://doi.org/10.22323/2.17030207 JCOM 17(03)(2018)A07 6
used [Wallach, Mimno and McCallum, 2009].5After multiple preliminary runs
with different numbers of topics (k), a model with ten topics was chosen for
analysis. The topics with the highest probability were also present in models with a
lower number of topics. More than ten topics did not provide further insights. The
following topics were identified (with the respective words being ordered by their
probability of appearing in the respective topics):
1. Models & Theories including the words “information model system paper
models theory systems process studies technology concept concepts based
complex scholarly”;
2. Open Access, including “access open information internet publishing web
research scientists data impact world system traditional process scholarly”;
3. Higher Education, containing the words “students education learning skills
university results understanding study higher high group knowledge nature
professional groups”;
4. Scientific Networks, including “social network community networks research
sciences field knowledge collaboration disciplines based approach
production fields patterns”;
5. Climate Change, with “climate change global political community important
policy cultural differences activities perceptions risk data focus human”;
6. Scientists & the Public, containing “public scientists understanding
engagement issues technology policy activities work article research
communicate people general communicating”;
7. Empirical Analysis, with “research study information analysis results studies
data findings researchers paper academic countries online content evidence”;
8. News Media and Public Communication, containing “media social
knowledge news society role article discourse context study political popular
cultural debate content”;
9. Applied Science Communication, including “research knowledge
development review health paper management process quality risk
important practice current practices future”; and
10. Citation & Publication Analysis, with “journals articles journal published
authors citation article publication results publications language number
literature international impact”
In the final step, the identified communities were combined with the topics
identified via LDA in order to provide a more robust description, and
differentiation, of the co-citation communities.
5α=50/Tand β=0.01. Hyperparameters were optimized every 20 iterations after 50 burn-in
iterations. Overall 1000 iterations were used to ensure that the log-likelihood stabilizes.
https://doi.org/10.22323/2.17030207 JCOM 17(03)(2018)A07 7
Identifying
co-citation
communities
Using co-citation analysis based on 1238 unique references and 1051 of our original
documents, we found eleven co-citation communities in the scholarly literature on
science communication. Subsequently, we assigned individual publications to
these communities, and by employing latent dirichlet allocation (LDA) on these
publications’ abstracts, we could identify the major topics which are used in the
respective communities.
Table 1. Description of co-citation-based communities, their size, typical publications and
topics.
Community N Typical Publications Characteristic Topics
Sociology of
Science
188 Latour [1987]: Science in Action
Kuhn [1962]: The Structure of Scientific Revolutions
Merton [1973]: The Sociology of Science
Models & Theories /
Citation &
Publication Analysis
Science debates
and the role of
journalism
208 Nisbet & Scheufele [2009]: What’s next for science communication?
Boykoff & Boykoff [2004]: Balance as Bias
Nisbet et al. [2003]: Framing Science
Climate Change /
News Media &
Public
Communication
Dissemination of
Science via Media
247 Gregory & Miller [1998]: Science in Public
Nelkin [1995]: Selling Science
Hilgartner [1990]: The dominant view of popularization
News Media &
Public
Communication
Scholarly
Communication
80 Garvey [1979]: Communication: The Essence of Science
Borgman [2007]: Scholarship in the Digital Age
Tenopir & King [2000]: Towards Electronic Journals
Open Access /
Citation &
Publication Analysis
Scientists as
Communicators
94 Davies [2008]: Constructing communication: Talking to scientists
about talking to the public.
Besley & Tanner [2011]: What science
communication scholars think about training scientists to
communicate
Bonetta [2007]: Scientists Enter the Blogosphere
Scientists & the
Public
Media effects on
the broader
public
46 Nisbet et al. [2008]: Knowledge, reservations, or promise? A media
effects model for public perceptions of science and technology
Miller [1983]: Scientific Literacy
Lee & Scheufele [2006]: The influence of knowledge and deference
toward scientific authority: A media effects model
Empirical Analysis
From PUSto PEST 209 Bauer et al. [2007]: What can we learn from 25 years of PUS survey
research? Liberating and expanding the agenda.
Irwin & Wynne [1996]: Misunderstanding science?
Miller [2001]: Public understanding of science at the crossroads
Scientists & the
Public
Science
Education
54 Bell et al. [2009]: Learning Science in Informal Environments
Ziman [2000]: Real science: What it is, and what it means
Falk & Dierking [2000]: Learning from Museums
Higher Education
Open Access and
digital publishing
23 Lawrence [2001]: Online or invisible?
Johnson [2002]: Institutional Repositories
Antelman [2004]: Do open access articles have a greater research
impact?
Open Access
Information
networks in
science
26 Shannon & Weaver [1949]: The Mathematical Theory of
Communication.
Girvan & Newman [2002]: Community structure in social and
biological networks
Watts & Strogatz [1998]: Collective dynamics of ’small-world’
networks
Scientific Networks /
Models and
Theories
Applied Science
Communication
63 Jasanoff [1990]: The Fifth Branch, Science Advisers As Policy Makers.
Fischhoff [1995]: Risk perception and communication unplugged:
twenty years of process
Slovic [1993]: Perceived risk, trust, and democracy
Applied Science
Communication
https://doi.org/10.22323/2.17030207 JCOM 17(03)(2018)A07 8
The analyzed publications cover a broad topical spectrum. They include the
communication of science to outside audiences via new media and science
education as well as scholarly communication and bibliometric studies (see
Table 1). To identify the communities, we combined the results of the co-citation
analysis with the results of the topic models (see Figure 1). Based on the results, the
following subcommunities could be identified:
1. ‘Sociology of Science’ constitutes one of the largest communities with 188
publications. This community typically cites publications analyzing
sociological aspects of science: on the macro-level, facets like the influence of
social communities for the development of entire scientific paradigms and for
the trajectory of science itself [Kuhn, 1962]; on the meso-level, the social
norms and mechanisms of scientific communities [Merton, 1973]; and on the
micro-level, the role of social influences on scientific work inside the
laboratory [Latour, 1987]. Accordingly, this community is strongly associated
with the “Models & Theories” topic, as well as with “Citation and Publication
Analysis”.
2. ‘Science Debates and the Role of Journalism’ represents one of the largest
communities, too, with 208 publications. This community typically quotes
relatively recent publications on the role of media and public debates for
science [Boykoff and Boykoff, 2004; cf. Fischhoff and Scheufele, 2013]. It
focuses on science debates in the public, recognizing that science
communication often, and maybe increasingly, takes place in a social, often
political environment [Scheufele, 2013; Scheufele, 2014]. It concentrates on
appropriate communication strategies to successfully communicate science in
such situations [Nisbet, Brossard and Kroepsch, 2003], and on fruitful ways to
improve science communication in the future [Nisbet and Scheufele, 2009]. It
can be characterized by the “News Media & Public Communication” topic
and by the “Climate Change” topic which has been a recent emphasis within
science communication [Schäfer, 2015].
3. The ‘Media Dissemination of Science’ community shows similarities to
“Science debates and the role of journalism” in that it is also large, containing
247 publications, and focuses on the “News Media & Public Communication”
topic. In contrast to the previous community, however, it represents more
classic texts focusing on, or sometimes criticizing, a more traditional
understanding of science communication via media in which the media play
the role of a disseminator [Gregory and Miller, 1998; Hilgartner, 1990].
4. ‘Scholarly Communication’, in contrast to the abovementioned communities,
is focused on inner-scientific, scholarly communication. The community is
medium-sized with 80 publications. It deals with the general role of
communication as an essential characteristic of science [Garvey, 2014], as well
as with formal scientific communication via publications and its current
changes towards digital publishing [Borgman, 2010; Tenopir and King, 2000].
5. ‘Scientists as Communicators’, another medium-sized community with 94
titles, concentrates on individual scientists as communicators. The
publications assembled here analyse, for example, the perceptions, aims and
strategies of scholars when they communicate with the public [Besley and
Tanner, 2011; Davies, 2008] and when they engage on online communication
https://doi.org/10.22323/2.17030207 JCOM 17(03)(2018)A07 9
[Bonetta, 2007; Bonetta, 2009]. Accordingly, the topic “Scientists & the Public”
characterizes this community best.
6. ‘Media Effects on the Broader Public’ is a small community which contains 46
publications. Its specific focus lies on the effects of media communication
about science on “lay” audiences, which it analyses on an individual level
using standardized methodology taken from psychology and communication
science. Early on, this community focused on cognitive effects, particularly
on the scientific literacy of the audience [Miller, 1983], whereas later, the
understanding of such a literacy broadened [Miller, 1998] and other,
non-cognitive perceptions of science were also included [Nisbet et al., 2002].
In line with the empirical focus of this community, it is characterized by the
“Empirical Analysis” topic.
7. ‘From PUS to PEST’ is a large community, consisting of 209 publications,
which is mainly concerned with the development of interactions between
scientists and the public. The publications assembled here often deal with the
underlying models of science communication and their development from
“Public Understanding of Science” (PUS) to more dialogical, participatory
modes of “Public Engagement with Science and Technology” (PEST) [Bauer
and Bucchi, 2007; Irwin and Wynne, 1996; Miller, 2001]. In comparison to the
“Media Dissemination of Science” community, the role of the media is less
focused upon here in favor of the broader interactions between science and
society.
8. ‘Science Education’ is a small community which contains 54 publications.
Research in this community mainly focuses on the educational aspects of
teaching science or distributing scientific knowledge in formal learning
contexts like schools or universities, or in informal ones like museums [Falk
and Dierking, 2000; cf. Bell, 2009]. Scientific literacy as a concept is discussed
critically in this community [Shamos, 1995]. In line with the general focus of
the community, the topic “Education” dominates.
9. ‘Open Access’ is a community which, like the “Scholarly Communication”
community, focuses on inner-scientific communication. With only 23
publications, however, it is smaller and focuses on a specific aspect of
scholarly communication: open access [Lawrence, 2001]. It weighs
advantages and disadvantages of ‘green’, ‘gold’ and other forms of open
access, such as increased visibility and a positive impact on research
[Antelman, 2004], and discusses ways to organize open access publications,
e.g. in repositories [Johnson, 2002].
10. ‘Information Networks in Science’ is another small community with 26
publications. Research in this community heavily relies on the mathematical
model of communication [Shannon and Weaver, 1949] and implements the
method of network analysis to identify communication structures in various
scientific disciplines and research fields [Leydesdorff and Rafols, 2009], or
social structures in school classes [Conlan et al., 2011]. In line with the
methodological focus of the community “Scientific Networks” and “Models
and Theories” are the strongest topics.
11. ‘Applied Science Communication’ is a medium-sized community with 63
publications, which is mainly concerned with applied science communication
in fields such as health and risk communication. Research in this community
https://doi.org/10.22323/2.17030207 JCOM 17(03)(2018)A07 10
focuses on science policy [Jasanoff, 1990], risk communication as well as on
citizens’ risk perceptions [Slovic, 1993]. Many studies belonging to this
community are practical in nature and have a direct impact on science
communicators such as environment or health professionals [Grossberndt,
van den Hazel and Bartonova, 2012]. As the typically cited literature in this
community indicates, “Applied Science Communication” is one of the most
important topics in this community.
Figure 1. Relevance of different topics in the co-citation-based communities. (figure shows
normalized probabilities, i.e. sum to 1 over all topics).
How have the
subcommunities
developed over
time?
Over time, science communication research has grown considerably. Within this
growing field, however, the eleven communities identified in the first step
developed differently. This is clearly visible in our data, which shows that the cited
literature has shifted away from the previously dominant ‘Sociology of Science’
community (see Figure 2). Early on, literature from the ‘Sociology of Science’
clearly was the most quoted in the field. But, as Figure 2 reveals, that there has been
a gradual increase in citations from other communities, most notably from those
focusing specifically on media communication and outside communication of the
scientific community. Overall the breadth of scholarship increased [cf. Schäfer,
2012], with communities like ‘Scientists as Communicators’, ‘Media Effects on the
Broader Public’, ‘From PUS to PEST’ or ‘Science Debates and the Role of
https://doi.org/10.22323/2.17030207 JCOM 17(03)(2018)A07 11
Journalism’ emerging and gaining in importance. As of now, there is a relative
dominance of communities that have (mediated) communication at their core:
‘Media Dissemination of Science’, ‘From PUS to PEST’, and ‘Science Debates and
the Role of Journalism’.
Figure 2. Relative importance of different communities over time (figure shows percentage
of all citations by community).
Even though the ‘Sociology of Science’ literature is quoted less often in relation to
other communities in recent years, the absolute number of citations from this
community has risen over time, along with the growth of the entire field. The
‘Sociology of Science’ has exhibited a steady growth over the last twenty years
together with the other three largest communities (see Figure 3). Still, however, the
gap between the ‘Sociology of Science’ and ‘Science Debates and the Role of
Journalism’ became larger in recent years. In 2014, the ‘Science Debates and the
Role of Journalism’ literature was quoted three times as often as the ‘Sociology of
Science’ literature.
In our third analytical step, we assess the proximity and interconnections between
the communities. After we classified the cited literature, we checked how often
literature from each community was cited together, i.e. in the same scientific
publication, with literature from the same or other communities. This allows us to
measure the relative distance between the communities, and to visualize the results
using multidimensional scaling (MDS) [Cox and Cox, 2000]. We used the vegan
package in R to calculate a dissimilarity matrix with the Jaccard index [Oksanen
et al., 2016].6
The MDS visualization shows a close proximity between the three communities
which are, currently, quoted most often (see Figure 4). ‘Science Debates and the
Role of Journalism’, ‘From PUS to PEST’, and ‘Media Dissemination of Science’ are
all concerned with aspects of mediated science communication, and are often
6Additionally, we conducted a hierarchical cluster analysis to assess which communities can be
grouped together. This analysis confirmed the visual interpretation of the MDS: the
communication-centric communities can be grouped together.
https://doi.org/10.22323/2.17030207 JCOM 17(03)(2018)A07 12
Figure 3. Relative importance of different communities over time (figure shows percentage
from the different communities).
quoted together in studies of science communication. ‘Applied Science
Communication’, ‘Science Education’, ‘Scientists as Communicators’, and ‘Media
Effects on the Broader Public’ are another group of communities that are proximate
and, accordingly, often cited together. The other four communities are more
distanced from these two groups. Interestingly, the ‘Sociology of Science’
community does not belong to one of the groups, but has a central position in the
research on science communication. This indicates that this literature is not
sistematically quoted together with literature from specific other communities, but
still of importance for all other communities.
Discussion Using co-citation analysis allowed us to identify eleven distinct subcommunities
within science communication research. These communities are of varying size,
characterized by different research foci and epistemological traditions, and have
developed differently in the last 20 years.
The results for all three research questions — i.e. regarding the existing
communities, their development and their proximity to each other - indicate a
number of distinct developments. The field has shifted away from the ‘Sociology of
Science’, a subcommunity that is deeply rooted in sociology and has focused
strongly on the scientific system and its actors, towards communities which are
more media- and communication-centric. Overall, however, the literature of the
‘Sociology of Science’ community is still quoted often. This can be explained with
the most quoted literature from this community. Classics such as Latour [1987],
Kuhn [1962] and Merton [1973], which are basic references for many science
communication scholars, and accordingly, quoted from various communities.
Empirically, this can be observed in the MDA as an isolated yet central position of
the ‘Sociology of Science’ community literature in the SSC. It is also evident when
the absolute numbers of citations are analyzed, which were still rising over time for
https://doi.org/10.22323/2.17030207 JCOM 17(03)(2018)A07 13
Figure 4. Proximity of the different communities (figure shows result of multidimensional
scaling analysis).
the ‘sociology of science’ — even though the rise of communication- and
media-centric was even more pronounced.7
Regarding the development of science communication research, our analysis of
citation networks shows that the research field has differentiated itself over the
years. Not only did the communication- and media-centric communities emerge
and rise in importance, educational and practitioner-oriented literature became
more important as well. Still, however, our findings suggest that the
communication-centric communities have become the core of the field, accounting
for over 50% of all citations in 2014. This figure could rise even further, as the
literature from these communities is more recent than that from other communities.
It has mostly been published in the last decade, and the future has to show if it will
reach a half-life period similar to the ‘Sociology of Science’ literature that has been
mostly published before the 1990s.
7Still, this data must be interpreted with caution because the literature of the ‘Science debates and
the role of journalism’ community is still very new and citations have most likely not yet reached
their half-life [Abt, 1998].
https://doi.org/10.22323/2.17030207 JCOM 17(03)(2018)A07 14
It will be interesting to see whether these communities will move even closer
together in the future — which would be a clear sign of a maturing, and
increasingly emancipated, field. Our analysis has shown that the communities
have already come closer, and cover a wide range of methods and topics. Science
communication research has become a well-developed research field, at least with
regards to the indicator we have used here: scholarly citation networks. The future
development of the other two axes of disciplinary development, i.e. of institutional
and epistemological characteristics, will have to show if the field can further its
institutionalization and, maybe, move towards a distinct discipline. Currently, it is
too early for a final assessment.
These results come with some caveats, however, as this study has a number of
limitations. The first and most obvious one is that we have only focused on the
scholarly communication to assess the development of the field. Therefore, we
caution for early conclusions, and advice to further investigate the other ‘axes’
mentioned above.
Another possible weakness is the use of only two keywords at the outset of the
study, and of one of them being “scientific communication”. Even though we
checked the keyword seeds for potential biases, this strategy can be debated and
analyses using other, alternative strategies would be helpful. They could use more
specific keywords such as “science journalism”, for example, to select the initial
article sample, as there may be a considerable amount of publications not explicitly
mentioning “science communication” or “scientific communication” as a keyword.
A different strategy, which is typically implemented in bibliometric studies, would
be to use a sample based on articles from specific journals. While this might have
been appropriate for the analysis of a different, more mature field or discipline. At
the current state of research on science communication, however, it seemed
inappropriate as the respective literature is being published in a wide range of
disciplinary journals. Nonetheless, a variety of approaches would certainly be
helpful to describe the characteristics and developments of a research field as
diverse and dynamic as studies on science communication.
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Authors Adrian Rauchfleisch, Ph. D., is Assistant Professor at the Graduate Intitute of
Journalism at the National Taiwan E-mail: adrian.rauchfleisch@gmail.com.
Mike S. Schäfer, Dr., is Professor of Science Communication at the University of
Zurich. E-mail: m.schaefer@ikmz.uzh.ch.
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Attribution — NonCommercial — NoDerivativeWorks 4.0 License.
ISSN 1824-2049. Published by SISSA Medialab. jcom.sissa.it
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... However, recent studies portray environmental communication-and related fields, like science and climate communication-as narrowly focused on traditional communication disciplinary topics, especially mass media and journalism (Pleasant et al., 2002;Comfort and Park, 2018;Rauchfleisch and Schäfer, 2018;Agin and Karlsson, 2021). Due to their design, these studies may not provide a full picture of the field and its evolution. ...
... Using a similar methodology as the current study, Rauchfleisch and Schäfer (2018) conducted a co-citation analysis of the science communication literature from 1996-2016 using the Scopus database, which has been cited as one of the most comprehensive, second only to Google Scholar (Martín-Martín et al., 2018) (The latter, however, delivers inconsistent search results (Gusenbauer and Haddaway, 2020), and suffers from poor quality control (Delgado López-Cózar et al., 2019) and meta-data (Jacsó, 2010)). ...
... Like Rauchfleisch and Schäfer (2018), we also use unsupervised machine learning to analyze text and identify topics and themes characteristic of articles included in this analysis. Topic modeling is a form of unsupervised machine learning that finds broad themes based on words, also known as "topics," in a collection of documents (Maier et al., 2018;Blei et al., 2003). ...
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This book is written for members of the scholarly research community, and for persons involved in research evaluation and research policy. More specifically, it is directed towards the following four main groups of readers: – All scientists and scholars who have been or will be subjected to a quantitative assessment of research performance using citation analysis. – Research policy makers and managers who wish to become conversant with the basic features of citation analysis, and about its potentialities and limitations. – Members of peer review committees and other evaluators, who consider the use of citation analysis as a tool in their assessments. – Practitioners and students in the field of quantitative science and technology studies, informetrics, and library and information science. Citation analysis involves the construction and application of a series of indicators of the ‘impact’, ‘influence’ or ‘quality’ of scholarly work, derived from citation data, i.e. data on references cited in footnotes or bibliographies of scholarly research publications. Such indicators are applied both in the study of scholarly communication and in the assessment of research performance. The term ‘scholarly’ comprises all domains of science and scholarship, including not only those fields that are normally denoted as science – the natural and life sciences, mathematical and technical sciences – but also social sciences and humanities.
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