EDITORIAL
Bridging the divide between qualitative and
quantitative science studies
Loet Leydesdorff1
, Ismael Ràfols2
, and Staša Milojevic(cid:1)3
1Amsterdam School of Communication Research (ASCoR), University of Amsterdam,
PO Box 15793, 1001 NG Amsterdam, The Netherlands
2Centre for Science and Technology Studies (CWTS), Leiden University, Leiden,
The Netherlands & SPRU (Science Policy Research Unit), University of Sussex, ROYAUME-UNI
3Center for Complex Networks and Systems Research, The Luddy School of Informatics,
Computing, and Engineering, Indiana University, Bloomington, Etats-Unis
1.
INTRODUCTION
In January 2019, the Editorial Board of the Journal of Informetrics decided to resign following a
series of disagreements with Elsevier. In collaboration with the International Society for
Scientometrics and Informetrics (ISSI) and MIT Press, the Editorial Board thereupon launched this
journal: Études scientifiques quantitatives (QSS). The launch of QSS offers an opportunity to rethink
the contents and research agenda of the journal, and marks a turn from the focus on “metrics” to
science studies. Such a shift, reflected also in the name change, indicates the intention to seek
closer connections with colleagues in “qualitative science and technology studies” and take more
distance from journals focusing on specialist “metrics” (Milojevic(cid:1)& Leydesdorff, 2013).
The goal of this special issue is to explore the relations among and promote conversations between
quantitative science studies and neighboring fields. To this end, we invited a number of colleagues
conducting research relevant to this theme to articulate the relations between their research and QSS,
and to formulate challenges and research agendas for synergies between qualitative and quantitative
approaches in the broad area of Science and Technology Studies (STS), science-policy analyses,
innovation studies, the sociology of science, the science of science, and related domains. Their
response generated 11 articles and one letter that provide a rich panorama of views and exciting ideas
for building bridges and pursuing research agendas that have the potential to advance our knowledge
about science, scientific knowledge production, and the scientific workforce, as well as to promote
the responsible and sustainable usage of metrics for evaluation and policy.
2. THE “DIVIDE BETWEEN QUALITATIVE AND QUANTITATIVE” IN SCIENCE STUDIES
The idea of a main “divide” between qualitative and quantitative STS originated in relatively recent
studies that examined the relationship between qualitative and quantitative STS empirically.
Leydesdorff and Van den Besselaar (1997) argued on the basis of aggregated citation relations
among journals that three main groups of journals can be distinguished: one more specifically
qualitative oriented (par exemple., Social Studies of Science), one specifically focusing on quantitative
science studies (par exemple., Scientometrics), and a third interfacing between quantitatively oriented
journals and innovations studies (par exemple., Research Policy). From the latter perspective, cependant,
Martine, Rossignol, and Yegros-Yegros (2012, p. 1194) stated that
STS today is a rather divided community, with quantitative scientometrics and qualitative STS
researchers operating largely in isolation from one another, one or two individual exceptions
notwithstanding. The qualitative side of STS continues to expand its work on technology
(including constructive technology assessment) and innovation, with the original programme
un accès ouvert
journal
Citation: Leydesdorff, L., Ràfols, JE., &
Milojević, S. (2020). Bridging the divide
between qualitative and quantitative
science studies. Quantitative Science
Études, 1(3), 918–926. https://est ce que je.org/
10.1162/qss_e_00061
EST CE QUE JE:
https://doi.org/10.1162/qss_e_00061
Auteur correspondant:
Loet Leydesdorff
loet@leydesdorff.net
Gestion des éditeurs:
Loet Leydesdorff, Ismael Rafols,
and Staša Milojević
droits d'auteur: © 2020 Loet Leydesdorff,
Ismael Ràfols, and Staša Milojević.
Publié sous Creative Commons
Attribution 4.0 International (CC PAR 4.0)
Licence.
La presse du MIT
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
q
s
s
/
un
r
t
je
c
e
–
p
d
je
F
/
/
/
/
1
3
9
1
8
1
8
7
0
0
0
5
q
s
s
_
e
_
0
0
0
6
1
p
d
.
/
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Bridging the divide between qualitative and quantitative science studies
of work analysing the social influences on the content of science having diffused into the
mainstream and now attracting less interest. En même temps, scientometric research has
been moving beyond science into areas previously the domain of traditional sociology (tel
as innovation and the analysis of social networks within and between organisations), aussi
as forming links with information science (as reflected, Par exemple, in the recent creation of
the Journal of Informetrics).
On the basis of studying 136 chapters in both quantitative and qualitative handbooks of
études scientifiques et technologiques, Milojevic(cid:1), Sugimoto, et autres. (2014) concluded that “a great
divide” has structured STS intellectually. Cependant, these authors added that
[o]ne of the interesting findings of this study is the identification of chapters of shared
interest across the qualitative and quantitative divide and the nuanced differences when
it comes to studying the topics covered in these chapters: technologie, gender and policy.
The discussion about a divide between qualitative and quantitative STS is by no means new to
the field. In December 1987, Par exemple, a workshop was organized by John Irvine, Anthony van
Raan, and one of us (Leydesdorff ) on “the relations between qualitative theory and scientometric
methods in science and technology studies.” This resulted in a special issue of Scientometrics in
1989 containing more than 300 pages (vol. 15, issues 5–6, pp. 333–631).
At the workshop, John Irvine and Ben Martin (1989) contributed a paper entitled “International
comparisons of scientific performance revisited,” which offered new perspectives on the measure-
ment of national research performance. Michel Callon and his coauthors (Françoise Bastide and
Jean-Pierre Courtial) presented the co-word model (Bastide, Courtial, & Callon, 1989), et
Anthony van Raan presented a paper (coauthored with Harry Peters) entitled “Dynamics of a
scientific field analysed by co-subfield structures” (van Raan & Peters, 1989) These three programs
étaient, entre autres, elaborated in the decades since. In the introduction to the special issue,
Leydesdorff, Irvine, and Van Raan (1989, p. 333) formulated as follows:
There is growing recognition of the need to integrate qualitative theorizing in the philoso-
phy, sociology and history of science with the quantitative perspectives provided by scien-
tometric studies. D'une part, the use of scientometric indicators in policy analysis has
stimulated debates on what exactly various indicators employed indicate, given the signif-
icant conceptual and technical problems that exist in measurement. On the other hand, le
increased availability of large data-bases challenges researchers in the field of science and
technology studies (S & TS) to test more rigorously their hypotheses concerning the various
aspects of scientific and institutional developments.
In a recent handbook of qualitative STS, Wyatt, Milojevic(cid:1), et autres. (2017, p. 87) formulated
the following evaluation of research efforts bridging the divide:
Scientometrics and qualitative approaches within STS share a common origin, even if
they have grown apart over the past decades in terms of research practices, norms
and standards. Different skills are needed, and the epistemological assumptions are also
different. Cependant, both quantitative and qualitative STS have always shared a deep
commitment to the empirical study of science and technology, and practitioners of both
can be reflexive about their own knowledge production practices.
In sum, although there is empirical evidence for a divide between qualitative and quanti-
tative STS, one can also find efforts to bridge this gap over the past decades.
Études scientifiques quantitatives
919
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
q
s
s
/
un
r
t
je
c
e
–
p
d
je
F
/
/
/
/
1
3
9
1
8
1
8
7
0
0
0
5
q
s
s
_
e
_
0
0
0
6
1
p
d
.
/
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Bridging the divide between qualitative and quantitative science studies
3. THE INTELLECTUAL ORIGINS OF THE DIVIDE
Notwithstanding these common interests in bridging the divide, the tensions between qualitative
and quantitative science studies have been constitutive of the field. In a review article entitled
“Quantitative measures of communication in science: A critical overview,” David Edge (1979,
p. 114)—at the time the editor of Social Studies of Science—for example, criticized quantitative
science studies in the following strong wording:
One is tempted to say that formal communication in science is “the tip of the iceberg,” were
it not for two facts: (un) the “tip” is very large, extensive and important; et (b) there is every
indication that the “tip” is radically different in kind from what is “below the waterline.”
(Perhaps “the soft underbelly of science” might be a more appropriate metaphor!)
Edge’s programmatic perspective of “following the actors” was committed to the “strong
program” in the sociology of scientific knowledge (Bloor, 1976). In this sociology of scientific
connaissance, it is claimed that the content of science can be explained in terms of sociocognitive
interests. From this perspective, the sciences can be considered as belief structures attributed to
communautés. The evidence supporting the claim of truth in science is constructed (Fuller, 2018).
These constructs can be deconstructed. Cependant, an analyst cannot then escape from the
reflexive conclusion that one’s own knowledge claim is also constructed; all debates and
arguments thus tend to become matters of interests and opinion (par exemple., Woolgar, 1988).
Unlike an anthropological focus on practices, the study of science as a publication structure
allows for a more distanced approach. The dynamics of the literature are sometimes very different
from that of science as a social process. It seems to us that this “double hermeneutics” in terms
of formal and nonformal communications is unavoidable in science studies (Giddens, 1976)
because of the dynamics of the literature enabling us to move back and forth between contexts
of discovery and justification (par exemple., Myers, 1985). The textual layer (the library, the archive, etc.) est
structured with reference to disciplines that also operate as selection mechanisms. The practices
generate variation and novelty, that is reflected in the texts (Callon, Loi, & Rip, 1986; Callon,
Courtial, et coll., 1983), and the discursive layer has a dynamic of its own (Gilbert, 1977;
Mulkay, Potter, & Yearley, 1983).
The context of application in research evaluations, technology assessments, and science and tech-
nology (S&T) policy analyses has added a third “mode of knowledge production” to the field of STS
during the last decades (Gibbons, Limoges, et coll., 1994). Both qualitative and quantitative science
studies have been challenged by priority programs such as the National Science Foundation’s
“Science of Science and Innovation Policies,” now replaced by the program “Science of Science:
Découverte, Communication, and Impact” (cf. Husband Fealing, voie, Marburger III, & Shipp,
2011; Marburger III, 2005). The European Framework and Horizons Programs call on STS from
the perspective of applications. Perhaps the pressure of funding agencies on this field has in the mean-
time become a unifying factor, because one often needs a variety of perspectives in studies with
normative objectives and implications. Cependant, these are empirical questions.
While the differing contexts can be distinguished analytically, they are interacting in the prac-
tices which are under study when “following the actors.” Pickering (1995), Par exemple, proposed
the metaphor of a “mangle of practice.” In the so-called “sociology of translations,” heterogeneous
réseaux (representing people, texts, cognitions, funding, and subjects of study (par exemple., scallops
[Callon, 1986]) are analyzed in terms of translations from one co-word map into another
(Callon et al., 1983). Such heterogeneity—including, Par exemple, also “nonhumans”— provides
resources for revisions and for changes.
Études scientifiques quantitatives
920
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
q
s
s
/
un
r
t
je
c
e
–
p
d
je
F
/
/
/
/
1
3
9
1
8
1
8
7
0
0
0
5
q
s
s
_
e
_
0
0
0
6
1
p
d
/
.
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Bridging the divide between qualitative and quantitative science studies
It seems to us that this focus on “heterogeneity” at both the substantive and methodological
levels is not so different from Merton’s (1948) call for middle-range theories and pluriformity.
À l'époque, Merton (1948) made two points that are still relevant to the issue, as follows:
1. “[…] as a matter of plain fact the theorist is not inevitably the lamp lighting the way to
new observations. The sequence is often reversed. Nor is it enough to say that research
and theory must be married if sociology is to bear legitimate fruit. They must not only
exchange solemn vows—they must know how to carry on from there. Their reciprocal
roles must be clearly defined.” (p. 515)
“What we have said does not mean that the piling up of statistics of itself advances
théorie; it does mean that theoretic interest tends to shift to those areas in which there
is an abundance of pertinent statistical data.” (pp. 512f.)
2.
We intend this issue as a contribution to the clarification and definition of the reciprocal roles of
quantitative and qualitative STS by focusing on research at the edge between the two approaches.
4. THE ORGANIZATION OF THE ISSUE
The contributions to this special issue have been grouped into four themes with three papers
chaque: (un) describing and questioning the divide between quantitative and qualitative science
études, (b) the use of numbers in decision-making addressing the usage of quantitative results
in the context of policy-making and research evaluations, (c) perspective and bridges show-
casing three currently very active research topics that attract researchers and scholars from a
wide range of science studies fields, et (d) future research programs laying out roadmaps for
the types of questions and approaches that can move the field forward.
4.1. Describing and Questioning the Divide
The three contributions in the first section of this collection address the divide from social, textual,
and epistemic perspectives, respectivement. D'abord, Geoff Bowker contributes a letter entitled “Numbers
or no numbers in science studies.” The author narrates his experiences with the chasm that opened
between “quals” (“‘ethnomonsters”) and “quants” (“quantheads”) as political battles over hiring
decisions erupted between the two camps of a sociology department. During such episodes, le
arguments of each side can be ignored by the other on the basis of legitimations other than scholarly
ones. Bowker (2020) argues for the importance of recognizing the complementary strengths of dif-
ferent approaches and for avoiding falling into dogmatic controversies.
The divide between qualitative and quantitative STS is empirically studied in a paper by Douglas
Kang and James Evans entitled “Against method: Exploding the boundary between qualitative and
quantitative studies of science.” The authors compare publications in qualitative and quantitative
sciences studies journals. The semantic analysis by Kang and Evans (2020) shows that qualitative
and quantitative analyses build on opposite normative worlds: Whereas qualitative studies dwell
on concepts such as “social,” “theory,” “political,” and “context,” quantitative analyses focus on
“performance,” “measure,” and “results.” The authors argue that these literatures have disparate
interests (both cognitively and politically) and are written for different audiences. They envisage
that the further development of computer technologies will ease the tensions.
Whereas the two previous papers described a divide in qualitative and quantitative terms,
respectivement, Harriet Zuckerman closes this section with a paper entitled “Is ‘the time ripe’ for quan-
titative research on misconduct in science?,” in which she analyzes the “why” of the problems
involved in integrating the two perspectives. The argument runs as follows: If one relies on statistics
Études scientifiques quantitatives
921
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
q
s
s
/
un
r
t
je
c
e
–
p
d
je
F
/
/
/
/
1
3
9
1
8
1
8
7
0
0
0
5
q
s
s
_
e
_
0
0
0
6
1
p
d
/
.
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Bridging the divide between qualitative and quantitative science studies
for making a qualitative argument, one risks making claims on the basis of data that can be decon-
structed from other perspectives. Official government statistics, Par exemple, are organized for an-
other objective. Using the case of misconduct in science, Zuckermann (2020) concludes that “a
healthy dose of skepticism is in order in evaluating both the findings of current quantitative studies
and of proposals for its remediation.”
4.2. Using Numbers in Decision-Making
Comme indiqué, a third context of applications has become constitutive of STS in terms of resources,
relations with clients, and legitimation (Gibbons et al., 1994). STS develops its own discourse
by analyzing among other things the discourses in the techno-sciences under study, and by
“translating” both these discourses into political and managerial contexts, comme la recherche
evaluations, technology assessments, and public debate. The three articles in the second section
explore the relationship between quantitative science studies and the use of numbers for decision-
making in these other contexts, including relations with industry and governments.
Quantification can be used and abused for justification in decision-making processes (Porter,
1996). The development of S&T indicators, cependant, has also led to controversies about their
utiliser. The feedback from policies and ideologies such as New Public Management have directly
influenced research agendas in scientometrics through consultancies and funding sources. In their
papier, entitled “The impact of J. D. Bernal’s thoughts in the science of science upon China:
Implications for today’s quantitative studies of science,” Yong Zhao, Jian Du, and Yishan Wu
discuss the contribution of John Desmond Bernal (par exemple., Bernal, 1939) to the “science of science”
and the ideological role that quantitative studies of science has played first in the Soviet Union, mais
also to this day in China. While the use of indicators for policy purposes has been associated in the
West with New Public Management and neoliberal policies (Burrows, 2012; Power, 2005), ces
indicators and a systems perspective were embraced by communist regimes, which at the time
believed in the virtues of central planning. Zhao, Du, and Wu (2020) plead for a reflection on these
alternative routes as a means to achieve a more harmonious integration between qualitative and
quantitative STS in other countries. Cependant, there has been much debate in recent years over the
potentially problematic consequences of the use of S&T indicators (Barré, 2019; Weingart, 2005),
particularly in evaluation studies (de Rijcke et al., 2016; DORA, 2015; Hicks, Wouters, et coll., 2015).
The two following contributions on the policy use of S&T indicators reflect on the conditions
ofuse of indicators in the research system and emphasize the importance of appropriate under-
standings of theoretical framings and policy contexts for the successful use of S&T indicators. Dans
their paper entitled “Powerful numbers: Exemplary quantitative studies of science that had policy
impact,” Diana Hicks and Kimberley Isett endorse the view that quantitative analysis may have a
positive impact on policies as an evidence base, but they note that the evidence “only rarely has a
notable policy impact.” Hicks and Isett (2020) further explore the conditions that enable “numbers”
to make a difference in decision-making. The study describes how the relevance, legitimacy, et
accessibility of the studies are important in the translation of scientific results to generate policy
impact—and how this “evidence” has both quantitative and qualitative components.
Thomas Heinze and Arlette Jappe use the sociology of professions to compare the contrasting
uses of bibliometrics in Dutch and Italian research evaluations.1 In this paper entitled
“Quantitative science studies should be framed with middle-range theories and concepts from
the social sciences,” Heinze and Jappe (2020) argue that differences in institutionalization can
explain the quality of the evaluations. In the Netherlands, Par exemple, research evaluation is
1 See also Jappe, Pithan, and Heinze (2018) on the difficulties of professionalization in evaluative scientometrics.
Études scientifiques quantitatives
922
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
q
s
s
/
un
r
t
je
c
e
–
p
d
je
F
/
/
/
/
1
3
9
1
8
1
8
7
0
0
0
5
q
s
s
_
e
_
0
0
0
6
1
p
d
.
/
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Bridging the divide between qualitative and quantitative science studies
controlled by professional experts, whereas Italy has a centralized model co-opted by academic
elites. The study is meant as an example of how quantitative science studies would benefit from
framing “their data and analyses with middle-range sociological theories and concepts in order to
advance our understanding of institutional configurations of national research systems.”
4.3. Perspectives and Bridges
Dans la section suivante, we turn to research topics in science studies that have been addressed from
more than a single perspective and thus offer opportunities for cross-fertilizations among dis-
courses. As Kang and Evans have shown, some topics are best addressed either by qualitative ap-
proaches (par exemple., more related to practices) or by quantitative approaches (par exemple., more related to
performance). Comme indiqué, Milojevic(cid:1) et autres. (2014) flagged programs and studies that were remarkably
competent in crossing the divide for substantive and intellectual reasons. Data infrastructure,
genre, and geography are analyzed here as examples of possible bridging functions between
disciplinary traditions.
The contribution by Christine Borgman entitled “Whose text, whose mining, and to whose
benefit?” reminds us that the possibility of conducting quantitative science studies depends on data
availability. The availability of data is mediated by infrastructure and a political economy that
makes this possible. Borgman (2020) explains that while academic scholarship is becoming
increasingly open to reading, it has not become more open to mining. This is problematic because
“scholarly information retrieval has degraded, from customized discipline-specific tools to generic
search engines” and, donc, data mining is necessary for searching information. The issue links
with “fake news” and “misconduct.” Borgman argues that research outcomes should be made
“open” to read and to mine—rather than having private companies controlling academic informa-
tion. Current studies are often shaped by data availability, lequel, among other things, tends to mar-
ginalize regions and disciplines with fewer economic resources (Vessuri, Guédon, & Cetto, 2014).
Mary Frank Fox’s review (entitled “Gender, science, and academic rank: Key issues and
approaches”) discusses gender inequalities in science and shows that scholarship in this topic
could benefit from different theoretical and methodological approaches. Fox aims to understand
the lower and slower promotion of women to full professor by focusing on (un) patterns of collab-
oration and (b) evaluative practices. Fox (2020) draws on empirical insights from surveys (par exemple., Fox
& Mohapatra, 2007), interviews (par exemple., Gaughan & Bozeman, 2016), and publication analysis (par exemple.,
Macaluso, Larivière, et coll., 2016), triangulating evidence in ways that make for robust scholarship.
Koen Frenken’s article entitled “Geography of scientific knowledge: A proximity approach”
shows how a topic such as “the process of rendering knowledge claims scientific” can draw on
and be enriched by combining insights from diverse disciplinary traditions. From economic geog-
raphie, Frenken adopts the notion of proximity (Boschma, 2005), and situates his approach by
building on the insights of STS and the sociology of scientific knowledge (Shapin, 1995). Le
author proposes a theoretical framework and various empirical avenues (open to both qualitative
and quantitative inquiry) to study the diffusion of knowledge claims and the analysis of scientists’
mobility. Frenken’s focus enables him to move back and forth between diverse traditions without
the readers even noticing this. Frenken (2020) thus provides a focus on the topic that successfully
creates bridges beyond the conventional silos.
4.4. Future Directions
The three papers in the last section of this issue make programmatic proposals. These papers,
as well as Kang & Evans (au-dessus de), propose agendas that seek to overcome the methodological
Études scientifiques quantitatives
923
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
q
s
s
/
un
r
t
je
c
e
–
p
d
je
F
/
/
/
/
1
3
9
1
8
1
8
7
0
0
0
5
q
s
s
_
e
_
0
0
0
6
1
p
d
.
/
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Bridging the divide between qualitative and quantitative science studies
dilemma of a choice between thick and situated versus thin and decontextualized approaches.
As Alberto Cambrosio, Jean-Philippe Cointet, and Alexandre Hannud Abdo explain in their
paper entitled “Beyond networks: Aligning qualitative and computational science studies”:
while thick descriptions of selected sites missed the configurational dimensions of the
collectives, resort to a few quantitative indicators to account for configurational complexity
destroyed for all practical purposes the very phenomena under investigation.
According to these authors, the research agendas point in different directions. The differences
suggest that methodological divergence is related to epistemological positions.
Cambrosio, Cointet, and Abdo’s interests lie in aligning quantitative empirical approaches with
the theorical frameworks of science studies. They argue that methods such as Actor-Network
Theory allow for cross-fertilizations between qualitative and quantitative approaches in STS.
They vindicate the tradition of science mapping using co-words (Callon et al., 1983, 1986) avec
its emphasis on heterogeneous networks, as against the mainstream citation-based and “clean”
(c'est à dire., mono-thematic) ontologies dominant in scientometrics. The authors envisage how advanced
network analysis tools and natural language processing allow for an engagement with sociological
theories in STS, such as translation theory.
The second article in this section is Henry Small’s paper, entitled “Past as prologue:
Approaches to the study of confirmation in science.” Small (2020) is interested in methods
for the confirmation of knowledge claims in the face of an “anti-science bias” in the sociology
of science. He shares a personal and rich recollection of the collision between Mertonian and
constructivist science studies during the 1970s and 1980s. The use of Bayesian statistics pro-
vides insights into the nature of support across a large part of the literature of knowledge
claims. While Small’s interest is about the “confirmation/disconfirmation” of facts, the method
he proposes can also be used for mapping whether and how certain organizations or funding
agencies support specific knowledge claims (Oreskes & Conway, 2011).
In their paper entitled “From indicators to indicating interdisciplinarity: A participatory
mapping methodology for research communities in-the-making,” Noortje Marres and Sarah
de Rijcke are interested in situating the insights of quantitative studies. Their point of departure
is the search for indicators of interdisciplinarity in artificial intelligence (AI). Given the multiple
interpretations of the notion of interdisciplinarity and the diverse understandings of AI, ils
propose to shift from indicators to indicating. In the journey to indicating, science mapping
appears as a useful interface, allowing analysts and engaged stakeholders to align their methods
with their interpretations of interdisciplinarity and AI. Marres and de Rijcke (2020) authors
contribute to recent debates on the need to contextualize quantitative approaches with the
participation of relevant stakeholders, which is particularly relevant in decision-making (Barré,
2010; Ràfols, 2019).
In sum, this collection of articles offers a panoramic view of the variety of current perspec-
tives on how quantitative science studies are related to qualitative science studies and neigh-
boring fields. Scholarly communication is specialist communication that needs to be
translated carefully when used in different contexts. It seems to us that both qualitative and
quantitative perspectives are needed in high-quality STS. To paraphrase the above quotation
from Merton (1948), the relations between qualitative and quantitative STS “should not only
remain solemn vows—one should know how to carry on from there.” These reciprocal roles
can then be elaborated in research designs and programs. The edge between qualitative and
quantitative approaches in STS has also been a source for our longer-term research programs.
Études scientifiques quantitatives
924
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
q
s
s
/
un
r
t
je
c
e
–
p
d
je
F
/
/
/
/
1
3
9
1
8
1
8
7
0
0
0
5
q
s
s
_
e
_
0
0
0
6
1
p
d
.
/
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Bridging the divide between qualitative and quantitative science studies
REMERCIEMENTS
We are grateful to the authors of the papers for their collaboration and to Sally Wyatt for her
participation in the initiative for this theme issue. Cassidy Sugimoto supported the project as
the President of ISSI. We are grateful to the Technische Informationsbibliothek (TIB) – Leibniz
Information Centre for Science and Technology for covering the APCs of the papers published
in this special issue.
COMPETING INTERESTS
The authors have no competing interests.
INFORMATIONS SUR LE FINANCEMENT
No funding was received for this research.
RÉFÉRENCES
Barré, R.. (2010). Towards socially robust S&T indicators: Indicateurs
as debatable devices, enabling collective learning. Research
Evaluation, 19(3), 227–231.
Barré, R.. (2019). Les indicateurs sont morts, vive les indicateurs!
Towards a political economy of S&T indicators: A critical over-
view of the past 35 années. Research Evaluation, 28(1), 2–6.
Bastide, F., Courtial, J.-P., & Callon, M.. (1989). Is it possible to use
review articles for the analysis of the dynamics of an area of
recherche? Scientometrics, 15(5–6), 535–562.
Bernal, J.. D. (1939). The social function of science. Cambridge,
MA: AVEC Presse (1967).
Bloor, D. (1976). Knowledge and social
imagery. Londres:
Routledge & Kegan Paul.
Borgman, C. L. (2020). Whose text, whose mining, and to whose
benefit? Études scientifiques quantitatives, 1(3), 993–1000.
Boschma, R.. (2005). Proximity and innovation: A critical assess-
ment. Regional Studies, 39(1), 61–74.
Bowker, G. C. (2020). Numbers or no numbers in science studies.
Études scientifiques quantitatives, 1(3), 927–929.
Burrows, R.. (2012). Living with the h-index? Metric assemblages
in the contemporary academy. The Sociological Review, 60(2),
355–372.
Callon, M.. (1986). Some elements of a sociology of translation:
Domestication of the scallops and the fishermen of St Brieux
Bay. In J. Loi (Ed.), Power, action and belief: A new sociology
of knowledge (pp. 196–229). Londres: Routledge & Kegan Paul.
Callon, M., Courtial, J.-P., Tourneur, W. UN., & Bauin, S. (1983). Depuis
translations to problematic networks: An introduction to co-word
analyse. Social Science Information, 22(2), 191–235.
Callon, M., Loi, J., & Rip, UN. (Éd.). (1986). Mapping the dynamics
of science and technology. Londres: Macmillan.
Cambrosio, UN., Cointet, J.-P., & Abdo, UN. H. (2020). Beyond net-
travaux: Aligning qualitative and computational science studies.
Études scientifiques quantitatives, 1(3), 1017–1024.
De Rijcke, S., Wouters, P.. F., Rushforth, UN. D., Franssen, T. P., &
Hammarfelt, B. (2016). Evaluation practices and effects of indicator
use—A literature review. Research Evaluation, 25(2), 161–169.
DORA.
(2015). San Francisco Declaration on Research
Assessment. https://sfdora.org/ (accessed April 10, 2020).
Edge, D. (1979). Quantitative measures of communication in science:
A critical overview. History of Science, 17, 102–134.
Fox, M.. F. (2020). Gender, science, and academic rank: Key issues
and approaches. Études scientifiques quantitatives, 1(3), 1001–1006.
Fox, M.. F., & Mohapatra, S. (2007). Social-organizational charac-
teristics of work and publication productivity among academic
scientists in doctoral-granting departments. Journal of Higher
Éducation, 78(5), 542–571.
Frenken, K. (2020). Geography of scientific knowledge: A proximity
approche. Études scientifiques quantitatives, 1(3), 1007–1016.
Fuller, S. (2018). Post-truth: Knowledge as a power game. London/
New York: Anthem Press.
Gaughan, M., & Bozeman, B. (2016). Using the prisms of gender
and rank to interpret research collaboration power dynamics.
Social Studies of Science, 46(4), 536–558.
Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., &
Trow, M.. (1994). The new production of knowledge: The dynamics
of science and research in contemporary societies. Londres: Sage.
Giddens, UN. (1976). New rules of sociological method. Londres:
Hutchinson.
Gilbert, G. N. (1977). Referencing as persuasion. Social Studies of
Science, 7, 113–122.
Heinze, T., & Jappe, UN. (2020). Quantitative science studies should
be framed with middle-range theories and concepts from the so-
cial sciences. Études scientifiques quantitatives, 1(3), 983–992.
Hicks, D., & Isett, K. R.. (2020). Powerful numbers: Exemplary quan-
titative studies of science that had policy impact. Quantitative
Science Studies, 1(3), 969–982.
Hicks, D., Wouters, P., Waltman, L., De Rijcke, S., & Ràfols, je.
(2015). Bibliometrics: The Leiden Manifesto for research metrics.
Nature, 520(7548), 429–431.
Husband Fealing, K., voie, J.. JE., Marburger III, J.. H., & Shipp, S. S.
(Éd.). (2011). The science of science policy: A handbook.
Stanford, Californie: Stanford Business Books.
Irvine, J., & Martine, B. (1989). International comparisons of scientific
performance revisited. Scientometrics, 15(5–6), 369–392.
Jappe, UN., Pithan, D., & Heinze, T. (2018). Does bibliometric research
confer legitimacy to research assessment practice? A sociological
study of reputational control, 1972–2016. PLOS ONE, 13(6).
https://doi.org/10.1371/journal.pone.0199031
Kang, D., & Evans, J.. (2020). Against method: Exploding the bound-
ary between qualitative and quantitative studies of science.
Études scientifiques quantitatives, 1(3), 930–944.
Études scientifiques quantitatives
925
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
q
s
s
/
un
r
t
je
c
e
–
p
d
je
F
/
/
/
/
1
3
9
1
8
1
8
7
0
0
0
5
q
s
s
_
e
_
0
0
0
6
1
p
d
/
.
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Bridging the divide between qualitative and quantitative science studies
Leydesdorff, L., & Van den Besselaar, P.. (1997). Scientometrics and
communication theory: Towards theoretically informed indica-
tors. Scientometrics, 38(1), 155–174. https://doi.org/10.1007/
bf02461129
Leydesdorff, L., Irvine, J., & Van Raan, UN. (1989). The relation be-
tween qualitative theory and scientometric methods in science
and technology studies. Scientometrics, 15(5–6), 333–631.
Macaluso, B., Larivière, V., Sugimoto, T., & Sugimoto, C. R..
(2016). Is science built on the shoulders of women? A study
of gender differences in contributorship. Academic Medicine,
91(8), 1136–1142.
Marburger III, J.. H. (2005). Wanted: Better benchmarks. Science,
308(May), 1087–1087.
Marres, N. & de Rijcke, S. (2020). From indicators to indicating inter-
disciplinarity: A participatory mapping methodology for research
communities in-the-making. Études scientifiques quantitatives, 1(3),
1041–1055.
Martine, B. R., Rossignol, P., & Yegros-Yegros, UN. (2012). Science
and technology studies: Exploring the knowledge base. Research
Policy, 41(7), 1182–1204.
Merton, R.. K. (1948). The bearing of empirical research upon the
development of social theory. American Sociological Review,
13(5), 505–515.
Milojevic(cid:1), S., & Leydesdorff, L. (2013). Information metrics (iMetrics):
A research specialty with a socio-cognitive identity? Scientometrics,
95(1), 141–157.
Milojevic(cid:1), S., Sugimoto, C. R., Larivière, V., Thelwall, M., & Ding,
Oui. (2014). The role of handbooks in knowledge creation and dif-
fusion: A case of science and technology studies. Journal de
Informetrics, 8(3), 693–709.
Mulkay, M., Potter, J., & Yearley, S. (1983). Why an analysis of sci-
entific discourse is needed. In K. D. Knorr & M.. J.. Mulkay (Éd.),
Science observed: Perspectives on the social study of science
(pp. 171–204). Londres: Sage.
Myers, G. (1985). Texts as knowledge claims: The social con-
struction of two biology articles. Social Studies of Science, 15,
593–630.
Oreskes, N., & Conway, E. M.. (2011). Merchants of doubt: How a
handful of scientists obscured the truth on issues from tobacco
smoke to global warming. New York: Bloomsbury.
Pickering, UN. (1995). The mangle of practice: Time, agency, et
science. Chicago: University of Chicago Press.
Porter, T. M.. (1996). Trust in numbers: The pursuit of objectivity in
science and public life. Princeton: Princeton University Press.
Power, M.. (2005). The theory of the audit explosion. In E. Ferlie, L. E.
Lynn Jr., & C. Pollit (Éd.), The Oxford handbook of public manage-
ment (pp. 326–344). Oxford: Presse universitaire d'Oxford.
Ràfols, je. (2019). S&T indicators in the wild: Contextualization and
participation for responsible metrics. Research Evaluation, 28(1),
7–22.
Shapin, S. (1995). Cordelia’s love: Credibility and the social studies
of science. Perspectives on Science, 3(3), 255–275.
Petit, H. (2020). Past as prologue: Approaches to the study of confir-
mation in science. Études scientifiques quantitatives, 1(3), 1025–1040.
van Raan, UN., & Peters, H. (1989). Dynamics of a scientific field ana-
lysed by co-subfield structures. Scientometrics, 15(5–6), 607–620.
Vessuri, H., Guédon, J.. C., & Cetto, UN. M.. (2014). Excellence or
qualité? Impact of the current competition regime on science
and scientific publishing in Latin America and its implications
for development. Current Sociology, 62(5), 647–665.
Weingart, P.. (2005). Impact of bibliometrics upon the science system:
Inadvertent consequences? Scientometrics, 62(1), 117–131.
Woolgar, S. (1988). Science. The very idea. Beverly Hills/London: Sage.
Wyatt, S., Milojevic(cid:1), S., Parc, H. W., & Leydesdorff, L. (2017). Le
intellectual and practical contributions of scientometrics to STS.
In U. Felt, R.. Fouché, C. Miller & L. Smith-Doerr (Éd.), Handbook
of science and technology studies (4th ed.) (pp. 87–112). Boston,
MA: AVEC Presse.
Zhao, Y., Du, J., & Wu, Oui. (2020). Impact of J. D. Bernal’s thoughts
in the science of science upon China: Implications for today’s
quantitative studies of science. Études scientifiques quantitatives, 1(3),
959–968.
Zuckermann, H. (2020). Is “the time ripe” for quantitative research on
misconduct in science? Études scientifiques quantitatives, 1(3), 945–958.
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
q
s
s
/
un
r
t
je
c
e
–
p
d
je
F
/
/
/
/
1
3
9
1
8
1
8
7
0
0
0
5
q
s
s
_
e
_
0
0
0
6
1
p
d
/
.
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Études scientifiques quantitatives
926
