RESEARCH ARTICLE
Beyond networks: Aligning qualitative and
computational science studies
Alberto Cambrosio1
, Jean-Philippe Cointet2
, and Alexandre Hannud Abdo3
1Department of Social Studies of Medicine, McGill University, Canada
2Sciences Po, Médialab, Paris, France
3Université Paris Est, LISIS, UMR 1326 INRA, Champs sur Marne, Marne-la-Vallée, France
Keywords: actor-network theory, computational science studies, hypergraphs, network analysis,
stochastic block models
ABSTRACT
This article examines the thorny issue of the relationship (or lack thereof ) between qualitative and
quantitative approaches in Science and Technology Studies (STS). Although quantitative methods,
broadly understood, played an important role in the beginnings of STS, these two approaches
subsequently strongly diverged, leaving an increasing gap that only a few scholars have tried to
ponte. After providing a short overview of the origins and development of quantitative analyses of
textual corpora, we critically examine the state of the art in this domain. Focusing on the availability
of advanced network structure analysis tools and Natural Language Processing workflows,
we interrogate the fault lines between the increasing offer of computational tools in search of
possible uses and the conceptual specifications of STS scholars wishing to explore the epistemic
and ontological dimensions of techno-scientific activities. Finalmente, we point to possible ways
to overcome the tension between ethnographic descriptions and quantitative methods while
continuing to avoid the dichotomies (social/cognitive, organizing/experimenting) that STS has
managed to discard.
l
D
o
w
N
o
UN
D
e
D
F
R
o
M
H
T
T
P
:
/
/
D
io
R
e
C
T
.
M
io
T
.
/
e
D
tu
q
S
S
/
UN
R
T
io
C
e
–
P
D
l
F
/
/
/
/
1
3
1
0
1
7
1
8
6
9
8
4
4
q
S
S
_
UN
_
0
0
0
5
5
P
D
/
.
F
B
sì
G
tu
e
S
T
T
o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3
1.
IN THE BEGINNINGS: A FEW “HISTORICAL” REMARKS
From the very outset, quantitative analyses of scientific and technological activities have accom-
panied the growth of the field presently known as Science & Technology Studies (STS). One has
just to recall the pioneering work by Derek De Solla Price (1963, 1965, 1975), and the develop-
ment of citation indexing and related methods such as cocitation analysis (Small, 1973). Yet,
intersections between quantitative approaches and conceptual developments in STS have
been more the exception than the rule. Infatti, these two domains have not always coexisted
peacefully. Early skirmishes include strongly worded criticism (“Why I am not a co-citationist”)
by David Edge (1977, 1979), the cofounder of STS’s flagship journal, Social Studies of Science.
While, in parte, the debate revisited the opposition between qualitative (ethnographic) E
quantitative approaches raging for decades within general sociology, in the case of STS the situ-
ation is more ambivalent. For instance, one of the foundational texts of the ethnographic turn in
STS, Laboratory Life (Latour & Woolgar, 1979), mobilized citation analysis, albeit informed by
semiotics, as part of its investigation of laboratory practices. Latour (1976) had in fact broached
this topic in an early paper presented at the First Annual Meeting of the Society for Social Studies
of Science in 1976. Yet, one of the most telling examples of the complex relationship that STS
entertains with quantitative methods is provided by an alternative (some would say complemen-
tary) approach to citation analysis, namely co-word analysis (Callon, Courtial, et al., 1983).
a n o p e n a c c e s s
j o u r n a l
Citation: Cambrosio, A., Cointet, J.-P.,
& Abdo, UN. H. (2020). Beyond networks:
Aligning qualitative and computational
science studies. Quantitative Science
Studi, 1(3), 1017–1024. https://doi.
org/10.1162/qss_a_00055
DOI:
https://doi.org/10.1162/qss_a_00055
Corresponding Author:
Alberto Cambrosio
alberto.cambrosio@mcgill.ca
Handling Editors:
Loet Leydesdorff, Ismael Rafols,
and Staša Milojević
Copyright: © 2020 Alberto Cambrosio,
Jean-Philippe Cointet, and Alexandre
Hannud Abdo. Published under a
Creative Commons Attribution 4.0
Internazionale (CC BY 4.0) licenza.
The MIT Press
Aligning qualitative and computational science studies
Championed by Michel Callon, co-originator with Bruno Latour of Actor-Network Theory (ANT),
co-word analysis was an attempt to develop a computational approach—provocatively termed
“qualitative scientometrics” (Callon, Legge, & Rip, 1986)—that would be consistent with the sociology
of translation (as ANT is also known). Co-word analysis was grounded in a model of scientific practices
as flexible activities likely to undergo rapid change (Callon, 1995). As both the outcome of research
activities and artifacts purposefully designed to enroll other scientists, scientific texts were analyzed
as peculiar assemblages of terms leading to specific ways of problematizing issues by defining the roles
assigned to relevant entities (researchers, substances, tools, and technologies). By tracing new associ-
ations between words, one could thus track the emergence of research fronts and capture their
dynamics. In contrast to the program of building a “science of science”—initially sketched by Price
(1963) and recently respecified by Fortunato, Bergstrom, et al. (2018) as an endeavor that “places
the practice of science itself under the microscope, leading to a quantitative understanding of the
genesis of scientific discovery, creativity, and practice and developing tools and policies aimed at
accelerating scientific progress”—co-word analysis strived to provide a coherent conceptual and
methodological framework to overcome the tension between ethnographic descriptions and quanti-
tative approaches often devised to answer different questions (Callon, 2001; Callon et al., 1983).
This attempt at developing a coherent framework was not prompted by an abstract yearning for
methodological consistency. Piuttosto, developments in the substantive domains investigated by
STS practitioners raised a new challenge as the natural and biomedical sciences underwent a pro-
found transformation of their working patterns. Large research structures such as consortia and
networks embodied a “collective turn” that, as ANT practitioners warned, should not be reduced
to a mere increase in the number of authors cosigning papers but, Piuttosto, encompasses the hetero-
geneous nature of these emerging collectives. Heterogeneity, in this context, refers to specific
activities that associate a variety of practitioners with a motley of emerging bio-clinical entities,
such as genes and mutations, as part of a continuing process of stabilization and (Rif)qualification
(Cambrosio, Bourret, et al., 2014). A search for new methods was prompted by the following
conundrum: While thick descriptions of selected sites missed the configurational dimensions of
the collectives, resorting to a few quantitative indicators to account for configurational complexity
destroyed for all practical purposes the very phenomena under investigation (Callon, 2001).
A number of developments channeled methodological innovation in quantitative approaches
to the analysis of techno-scientific activities. Primo, the collective turn was accompanied by a
massive growth in electronic publications and related digital archives that opened the way for
“metaknowledge” investigations of the heterogeneous components of scientific activities (Evans
& Foster, 2011). On the conceptual side, natural scientists (in particular physicists, biologists, E
computer scientists) became interested in investigating large-scale real-world networks. Since the
1970S, traditional social network analysis—often relying on the topological analysis of small-size
ego-networks sampled through surveys and interviews—had become an accepted subfield of
sociology (Freeman, 2004). In contrasto, the aforementioned natural scientists who migrated to
the field of complex network analysis promoted a new understanding of large-scale real-world
networks, showing that they shared a number of properties (per esempio., small-world effects, scale-free
natura) and could be analyzed using dedicated tools (per esempio., network morphogenesis models and
innovative network topology metrics; Barabási & Réka, 1999; Watts, 2004; Watts & Strogatz,
1998). Most relevantly for our present purpose, they introduced new computational methods
for the analysis of very large data sets, allowing the generic modeling of the structure of multiplex
networks (featuring various kinds of edges) and their dynamics (Mucha, Richardson, et al., 2010;
Palla, Barabási, & Vicsek, 2007; Rosvall & Bergstrom, 2010).
One could argue, as suggested by a reviewer of this text, that the contrast between earlier
citationist methods and network analysis was more a matter of conceptual background and
Quantitative Science Studies
1018
l
D
o
w
N
o
UN
D
e
D
F
R
o
M
H
T
T
P
:
/
/
D
io
R
e
C
T
.
M
io
T
.
/
e
D
tu
q
S
S
/
UN
R
T
io
C
e
–
P
D
l
F
/
/
/
/
1
3
1
0
1
7
1
8
6
9
8
4
4
q
S
S
_
UN
_
0
0
0
5
5
P
D
.
/
F
B
sì
G
tu
e
S
T
T
o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3
Aligning qualitative and computational science studies
research horizons than of technical differences. Infatti, while the former approach was largely
defined by its focus on research policy and evaluation, compounded by lack of reflexivity, IL
latter was centered on modeling and mapping the dynamics of knowledge production. But tech-
nical differences clearly mattered. In particular, in addition to prestructured categories such as
citations and authors’ names mobilized by bibliometrics, researchers engaged in network
analysis also began exploring semantic approaches, using Natural Language Processing (PNL)
to extract new sets of variables from texts. They replaced unidimensional data analyses based
on limited amounts of data or highly aggregated data sets, with broader analyses of heterogeneous
dati, resorting to relational approaches and complex data visualization tools. Obviously, Questo
contrast is an ideal-typical one, as there are several examples of hybrid approaches that, while rooted
in the citation tradition, borrowed insights from network analysis, embracing its analytical visualiza-
tion techniques and introducing innovative solutions such as overlay maps (Rotolo, Rafols, et al.,
2017; Van den Besselaar & Heimeriks, 2006). Ancora, it does capture significant trends in the field.
The new analytical approaches mobilizing advanced network structure analysis tools (Blondel,
Guillaume, et al., 2008; Palla et al. 2007) and NLP workflows (per esempio., Van Eck & Waltman, 2011)
were quickly embedded in a growing number of publicly available software platforms, ad esempio
ReseauLu (per esempio., Jones, Cambrosio, & Mogoutov, 2011), VOSviewer (Van Eck & Waltman, 2009),
or CorTexT (per esempio., Tancoigne, Barbier, et al., 2014; Weisz, Cambrosio, & Cointet, 2017). These plat-
forms allow investigators to explore relationships between heterogeneous entities (per esempio., diseases,
istituzioni, substances) within a single map. They use dimension scaling (Van Eck & Waltman,
2007) or force-directed graph layout algorithms (Fruchterman & Reingold, 1991) that depend only
on the topology of the network. This latter family of algorithms models each entity as an object
connected to other objects by more or less strong elastic springs, depending on the strength of
the links between entities. Repulsive forces are simultaneously used to prevent pairs of nodes from
getting too close. A dynamic positioning algorithm optimizes the position of all of the nodes in order
to minimize the stress of the layout. The proximity of two entities is not directly representative of the
specific strength of relationship between them but instead represents the overall set of relationships
of that entity and the other entities to which it is specifically linked.
Unsurprisingly, several ANT practitioners, in particular those working at the médialab estab-
lished at SciencesPo Paris in 2009 under the leadership of Bruno Latour (https://medialab.
sciencespo.fr/) but also in Amsterdam around the media studies program put forward by
Richard Rogers, quickly embraced the analytical opportunities offered by these new develop-
menti, albeit not without reservations. No doubt, new computational approaches did change
the conversation, redefining the relation between conceptual developments and quantitative
approcci, but they did not dispense with the aforementioned essential tension between con-
ceptual developments and quantitative approaches within STS. How so, and why?
2. FROM PAST TO PRESENT: ISSUES AND PROBLEMS
What are the main criticisms raised against the new computational approaches? A first issue has to
do with the fact that although people tend to conflate actor-networks with digital networks (even
more so in the era of social media), actor-networks and social networks are two very different kinds
of things (Latour, 2011). The fact that the term network can refer to four possible meanings,
namely “a conceptual metaphor, an analytic technique, a set of data, a sociotechnical system”
(Venturini, Munk, & Jacomy, 2019, P. 513) contributed to this confusion. Inoltre, as argued
in more detail in Cambrosio et al. (2014), network-like representations generated by the aforemen-
tioned software platforms tend to promote structural and strategic interpretations, rather than dy-
namic analyses. The dynamics of an actor-network have heterogeneous roots, cannot be equated
Quantitative Science Studies
1019
l
D
o
w
N
o
UN
D
e
D
F
R
o
M
H
T
T
P
:
/
/
D
io
R
e
C
T
.
M
io
T
.
/
e
D
tu
q
S
S
/
UN
R
T
io
C
e
–
P
D
l
F
/
/
/
/
1
3
1
0
1
7
1
8
6
9
8
4
4
q
S
S
_
UN
_
0
0
0
5
5
P
D
.
/
F
B
sì
G
tu
e
S
T
T
o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3
Aligning qualitative and computational science studies
to changes in the morphology of network representations, and are often accounted for by the sub-
sequent intervention of entities that were not included in the initial descriptions. Allo stesso modo, equat-
ing clusters on a map to collectives is questionable insofar as the latter cannot be reduced to the
presence of collaborative ties but, Piuttosto, are defined by the reorganization of work around emerg-
ing entities (Rabeharisoa & Bourret, 2009). Arguably, notions such as assemblage (DeLanda, 2016)
or “agencement,” with its distributed-agency undertones (Callon, 2017), are better suited to cap-
ture the dynamics of actor-networks than the network metaphor. In short, network mapping comes
with built-in epistemological models that are not necessarily compatible with STS research
agendas.
And yet, in spite of these openly acknowledged shortcomings, some ANT practitioners have
argued that because of its “figurative power,” network mapping using the aforementioned force-
directed layouts still represents a useful, viable strategy for exploring scientific and technological
activities, provided that one deploys them as components of an argument encompassing the
heterogeneous nature and the mutually defining elements that account for the dynamics of actor-
networks (Latour et al., 2012). While we have also implicitly adopted a similar position in our own
lavoro, arguing for instance that one could use networks to produce something different from net-
works, we now believe that the time has come to go beyond (digital) networks with their simple
dyadic graph representations connecting homogeneous entities (cioè., graphs that can only model
interactions as links connecting pairs of entities of the same type). In the remainder of this paper we
would like to discuss a few promising alternatives, such as hypergraphs, that more closely adhere
to the specifications of a sociology of translation. Hypergraphs offer means of exploring the spe-
cific assemblages of persons, skills, technologies, entities, istituzioni, organizations, and claims
that define a given collective and its distributed agency, as well as of accounting for its dynamics
and thus for the heterogeneous ways in which its diverse components are linked, forming higher
order groups and hierarchies that can eventually be qualified as part of different regimes of
engagement (Thévenot, 2006).
To our knowledge, the closest equivalent to an ANT-informed quantitative approach is provided
in an article by Shi, Foster, and Evans (2015). Explicitly mentioning the sociology of translation as its
main conceptual referent, the article models science as a dynamic hypergraph, contrasting single-
mode standard networks (per esempio., coauthorship or co-word networks) with the multimode character of
scientific activities and related “wanderings” across different kinds of things, namely people,
metodi, diseases, and substances. A case study of millions of abstracts from MEDLINE provides
strong empirical support for the viability and heuristic value of this approach (Guarda la figura 1 in Shi
et al. [2015] for a visualization of a random sample of the MEDLINE hypergraph). Conceptually, IL
article also resorts to James March’s well-known “garbage-can model” (Cohen, Marzo, & Olsen,
1972) to formalize techno-scientific assemblages and assembly processes as hypergraphs “in which
articles are hyperedges and contain nodes of several distinct types.”
To understand the originality of the model developed by Shi et al. (2015) it is useful to contrast
it with traditional mapping approaches that focus on a single analytical dimension, such as coau-
thorship ties as proxies for collaborative endeavors (Moody, 2004), the semantic structure of a
given field as elicited, for instance, by topic modeling (Griffith & Steyvers, 2004), or cocitation
networks of the core references of a given field (Small, 1999). Bourret, Mogoutov, et al. (2006)
have used network analysis to investigate the interplay between human and nonhuman actors,
but they did so by visually inspecting maps produced with a traditional dyadic model of interaction
rather than hyperedges. Other scholars have investigated the possible articulation of descriptions
and aggregates produced by different approaches, for instance by combining cocitation and
co-word analysis (Braam, Moed, & Van Raan, 1991; Zitt & Bassecoulard, 2006). Tuttavia, Essi
simultaneously appraised only two dimensions.
Quantitative Science Studies
1020
l
D
o
w
N
o
UN
D
e
D
F
R
o
M
H
T
T
P
:
/
/
D
io
R
e
C
T
.
M
io
T
.
/
e
D
tu
q
S
S
/
UN
R
T
io
C
e
–
P
D
l
F
/
/
/
/
1
3
1
0
1
7
1
8
6
9
8
4
4
q
S
S
_
UN
_
0
0
0
5
5
P
D
/
.
F
B
sì
G
tu
e
S
T
T
o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3
Aligning qualitative and computational science studies
In contrasto, Shi et al.’s (2015) model—which partly capitalizes on previous efforts to use hyper-
graphs to capture the joint sociosemantic dynamics of team formation (Taramasco, Cointet, &
Roth, 2010; see also Falk-Krzesinski, Börner, et al., 2010)—integrates a number of different com-
ponents of a scientific publication within a joint framework, thus allowing for the proper measure-
ment and modeling of the heterogeneous topological relationships between distinct types of
entities, such as authors and chemicals. One should note, Tuttavia, that insofar as the sets of en-
tities deployed by individual articles are reduced to a number of dyadic interactions between these
constituents, the resulting metaknowledge network does not provide an entirely satisfactory model
of the processes characterizing assemblages, such as upward causality (cioè., emergent properties),
downward causality (cioè., the effect of the whole on its constituents), or the degree of homogeni-
zation (DeLanda, 2016). Infatti, one wonders whether or to what extent sophisticated qualitative
theorizing of this kind of dynamic assembling processes can in fact be translated into quantitative
analyses. Arguably, rather than a conflation of qualitative and quantitative analyses, one should
develop a trading zone where they could cross-fertilize each other. The multidimensional corre-
lations that may be found, for instance, within manifolds consisting of more than two entities
(Baudot, Tapia, et al., 2019) certainly call for additional modeling efforts to identify heterogeneous
complex patterns as an alternative to tracing coarser correlations between phenomena elicited
from different analytical dimensions. In this respect, combining hypergraphs and stochastic block
models may offer a promising option insofar as the latter can be used to measure the likelihood
of any configuration of heterogeneous entities, as seen in recent inquiries that adopt a purely
quantitative approach (Shi & Evans, 2019) or include qualitative considerations (Abdo, Cointet,
et al., 2019).
The conversion of textual content into analytical data that can be mobilized for specific pur-
poses is a challenging process that largely accounts for the difficulties encountered by scholars
who have been attempting to develop quantitative models of the dynamics of scientific activities
compatible with the conceptual specifications of qualitative STS models. Although the content of
scientific databases is extremely rich, several issues have long plagued their exploitation. Primo, IL
principle of generalized symmetry (per esempio., Callon, 1984), has contributed to a “flat” treatment of
language that only takes into account associations between words irrespective of the qualities
attributed to them. In co-word analysis, for instance, articles are modeled as sets of keywords with
no hypothesis about their distinctive role in a given publication. Di conseguenza, experimental devices,
biological entities, or statistical apparatuses all receive equal treatment and attention. Yet, it is
crucial to distinguish between these different entities according to the (shifting) ontological cate-
gories to which they are assigned within a given experimental system or at a given point in time—
as noted by Rheinberger (2009), an epistemic entity can turn into a technological object. Their
propensity to mutually interact will depend critically on those broader attributes and categories,
each defining a different “mode of existence” (Simondon, 1958). Again, Shi et al. (2015) and also
Li, Zhu, and Chen (2009) offer an alternative to such flat modeling. The approach they champion,
Tuttavia, also needs to take into account domain-dependency; in other words, the “same” entities
mobilized in a given subfield differ from domain to domain, by the same token espousing multiple
ontologies1. Named-entity recognition algorithms (see Nadeau and Sekine (2007) for a review)
could be very useful for establishing the situated roles played by different textual markers within
scientific texts. Recent developments in NLP such as word embeddings that resort to neural archi-
tectures for training (Devlin et al., 2018) have pushed further the state-of-the-art performance in
terms of drugs, genes, prescriptions, and other kinds of named-entities recognition (Lample,
Ballesteros, et al., 2016). The automatic detection of relations between entities is increasingly
1 But see Ribes, Hoffman, et al. (2019) on “domain independence.”
Quantitative Science Studies
1021
l
D
o
w
N
o
UN
D
e
D
F
R
o
M
H
T
T
P
:
/
/
D
io
R
e
C
T
.
M
io
T
.
/
e
D
tu
q
S
S
/
UN
R
T
io
C
e
–
P
D
l
F
/
/
/
/
1
3
1
0
1
7
1
8
6
9
8
4
4
q
S
S
_
UN
_
0
0
0
5
5
P
D
/
.
F
B
sì
G
tu
e
S
T
T
o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3
Aligning qualitative and computational science studies
effective (Bekoulis, Deleu, et al., 2018), paving the way to more accurate ways to model how
these entities are enrolling each other beyond the joint co-occurrence of terms on which co-
word analysis relies. Put it differently, fine-grained NLP methods may allow us to better charac-
terize what the hyphen in co-word analysis stands for.
In addition to extracting specific entities (drugs, techniques, eccetera.) from scientific texts, macchine
can capture the context in which hypotheses, claims, or pieces of evidence are deployed. Scientific
articles have long been described by STS analysts as literary devices aiming at enrolling readers and
funneling their attention by carefully distributing claims and statements of different nature and
degree of generality in different sections of the text (Legge, 1983). Past experiments and general
considerations are often staged in introductory sections, evidence claims located in the results
section, promissory notes and future developments consigned to the concluding remarks.
Contextual information of this kind could be incorporated in the modeling of texts (Guo,
Korhonen, & Poibeau, 2011). Allo stesso modo, it is now possible to integrate the context or the general
purpose for which a given reference is being cited (Jurgens, Kumar, et al., 2018). For instance, È
a citation used to solidify an argument or as a contentious source of knowledge? Advanced depen-
dency methods and more largely AI-powered text analysis methods, including semantic hyper-
graphs, allow for the detection and characterization of claims (Menezes & Roth, 2019) beyond
the sole co-occurrence of words within the same sentence or paragraph. These methods open
the way to a more refined analysis of evidence construction modalities.
3. CONCLUDING REMARKS
In her book on “digital sociology,” Noortje Marres (2017) raises a number of interesting issues
that apply, mutatis mutandis, to STS. Faced with the question of whether the investigation of
digital regimes requires the development of new methods, she points to the presence of both
continuities and discontinuities between traditional and recent quantitative approaches. She
therefore pleads for the adoption of “interface methods” that specifically interrogate the relation
between different methodological traditions, including qualitative ones. Her proposal is a useful
antidote to the careless way in which “much computational social science projects simply go
along with whatever ontology, epistemology or methodology is wired in to the platforms,
packages or tools they use to capture, analyze and visualize data, without querying whether
and how they are appropriate to the research project at hand” (P. 187; see also Cambrosio
et al., 2014 for a similar argument). Given that many STS scholars have insisted on reflexivity as
a key aspect of their domain, it is actually surprising that this issue has not attracted more wide-
spread attention despite recent calls for a more balanced collaboration between ethnographers
and computational sociologists (Evans & Foster, 2019; Goldberg, 2015).
Beyond the lack of reflexivity lurks the thorny issue of the adequacy between quantitative
science studies and conceptual aspects of leading STS approaches, in particular ANT, with its
admittedly ambiguous terminological reference to “networks.” As mentioned in the introductory
section, while calculations, broadly understood, played an important role in the beginnings of STS,
quantitative and qualitative approaches subsequently strongly diverged, leaving an increasing gap
that only a few scholars have tried to bridge. Although developed for a different purpose, namely to
add judgment to the notion of calculative agency or, in other words, to “think in the same terms
Di (quantitative) calculations and (qualitative) judgments,” the notion of “qualculation” (Callon
& Legge, 2005), if applied reflexively to the present discussion, could provide a useful heuristic
for interrogating the fault lines between the increasing offer of computational tools in search of
possible uses, and the conceptual specifications of those brands of science studies interested in
exploring the epistemic and ontological dimensions of techno-scientific activities.
Quantitative Science Studies
1022
l
D
o
w
N
o
UN
D
e
D
F
R
o
M
H
T
T
P
:
/
/
D
io
R
e
C
T
.
M
io
T
.
/
e
D
tu
q
S
S
/
UN
R
T
io
C
e
–
P
D
l
F
/
/
/
/
1
3
1
0
1
7
1
8
6
9
8
4
4
q
S
S
_
UN
_
0
0
0
5
5
P
D
/
.
F
B
sì
G
tu
e
S
T
T
o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3
Aligning qualitative and computational science studies
ACKNOWLEDGMENTS
We would like to thank Loet Leydesdorff and a second, anonymous reviewer for their extremely
useful comments and suggestions.
COMPETING INTERESTS
The authors have no competing interests.
FUNDING INFORMATION
Research for this paper was made possible by a grant from the Canadian Institutes of Health
Research (MOP-142478).
DATA AVAILABILITY
This is a position paper, not an article based on original data.
l
D
o
w
N
o
UN
D
e
D
F
R
o
M
H
T
T
P
:
/
/
D
io
R
e
C
T
.
M
io
T
.
/
e
D
tu
q
S
S
/
UN
R
T
io
C
e
–
P
D
l
F
/
/
/
/
1
3
1
0
1
7
1
8
6
9
8
4
4
q
S
S
_
UN
_
0
0
0
5
5
P
D
/
.
F
B
sì
G
tu
e
S
T
T
o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3
REFERENCES
Abdo, UN. H., Cointet, J. P., Bourret, P., & Cambrosio, UN. (2019).
Domain-topic models with chained dimensions: Modeling the
evolution of a major oncology conference (1995–2017). arXiv preprint,
http://arxiv.org/abs/1912.13349
Barabási, UN. L., & Réka, UN. (1999). Emergence of scaling in random
networks. Scienza, 286, 509–512.
Baudot, P., Tapia, M., Bennequin, D., & Goaillard, J. M. (2019).
Topological information data analysis. Entropy, 21(9), 869.
Bekoulis, G., Deleu, J., Demeester, T., & Develder, C. (2018). Joint
entity recognition and relation extraction as a multi-head selection
problem. Expert Systems with Applications, 114, 34–45.
Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008).
Fast unfolding of communities in large networks. Journal of
Statistical Mechanics: Theory and Experiment, 2008(10), P10008.
Bourret, P., Mogoutov, A., Julian-Reynier, C., & Cambrosio, UN. (2006). UN
new clinical collective for French cancer genetics: A heterogeneous
mapping analysis. Scienza, Tecnologia, & Human Values, 31(4),
431–464.
Braam, R. R., Moed, H. F., & Van Raan, UN. F. J. (1991). Mapping of science
by combined cocitation and word analysis. IO. Structural aspects. Journal
of the American Society of Information Science, 42(4), 233–251.
Callon, M. (1984). Some elements of a sociology of translation:
Domestication of the scallops and the fishermen of St Brieuc Bay.
The Sociological Review, 32(1_suppl), 196–233.
Callon, M. (1995). Four models for the dynamics of science. In S.
Jasanoff, G. E. Markle, J. C. Petersen, & T. Pinch (Eds.). Handbook
of science and technology studies (pag. 29–63). Thousand Oaks, CA:
Sage Publications.
Callon, M. (2001). Les méthodes d’analyse des grands nombres
peuvent-elles contribuer à l’enrichissement de la sociologie du
travail? In A. Pouchet (Ed.). Sociologies du travail: Quarante ans
après (pag. 335–354). Paris: Elsevier.
Callon, M. (2017). L’emprise des marchés. Comprendre leur fonc-
tionnement pour pouvoir les changer. Paris: La Découverte.
Callon, M., Courtial, J. P., Turner, W. A., & Bauin, S. (1983). From
translations to problematic networks: An introduction to co-word
analysis. Social Science Information, 22(2), 191–235.
Callon, M., Legge, J., & Rip, UN. (1986). Qualitative scientometrics. In
M. Callon, J. Legge, & UN. Rip (Eds.). Mapping the dynamics of
science and technology (pag. 103–123). Houndmills: Macmillan.
Callon, M., & Legge, J. (2005). On qualculation, agenzia, and otherness.
Environment and Planning D: Society and Space, 23, 717–733.
Cambrosio, A., Bourret, P., Rabeharisoa, V., & Callon, M. (2014).
Big data and the collective turn in biomedicine: How should we
analyze post-genomic practices? Tecnoscienza, 5(1), 13–44.
Cohen, M. D., Marzo, J. G., & Olsen, J. P. (1972). A garbage can
model of organizational choice. Administrative Science Quarterly,
17(1), 1–25.
DeLanda, M. (2016). Assemblage theory. Edinburgh: Edinburgh
Stampa universitaria.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. B. (2018). Pre-
training of deep bidirectional transformers for language under-
standing. arXiv preprint, arXiv:1810.04805.
Edge, D. (1977). Why I am not a co-citationist. Society for Social
Studies of Science Newsletter, 2, 13–19.
Edge, D. (1979). Quantitative measures of communication in science:
A critical review. History of Science, 17, 102–134.
Evans, J. A., & Foster, J. G. (2011). Metaknowledge. Scienza, 331,
721–725.
Evans, J. A., & Foster, J. G. (2019). Computation and the sociological
imagination. Contexts, 18(4), 10–15.
Falk-Krzesinski, H. J., Börner, K., Contractor, N., Fiore, S. M., Hall,
K. L., … Uzzi, B. (2010). Advancing the science of team science.
Clinical and Translational Science, 3(5), 263–266.
Fortunato, S., Bergstrom, C., Börner, K., Evans, J., Helbing, D., …
Barabàsi, A.-L. (2018). Science of science. Scienza, 359(6379),
eaao0185.
Freeman, l. C. (2004). The development of social network analysis.
A study in the sociology of science. Vancouver: Empirical Press.
Fruchterman, T. M. J., & Reingold, E. M. (1991). Graph drawing by
force-directed placement. Software. Practice & Experience, 21(11),
1129–1164.
Goldberg, A., 2015. In defense of forensic social science. Big Data &
Società, 2(2), 2053951715601145.
Quantitative Science Studies
1023
Aligning qualitative and computational science studies
Griffith, T. L., & Steyvers, M. (2004). Finding scientific topics.
Proceedings of the National Academy of Sciences, 101(suppl 1),
5228–5235.
Guo, Y., Korhonen, A., & Poibeau, T. (2011) A weakly supervised
approach to argumentative zoning of scientific documents. In
Proceedings of the Conference on Empirical Methods in Natural
Language Processing (pag. 273–283). Associazione per il calcolo
Linguistica.
Jones, D., Cambrosio, A., & Mogoutov, UN. (2011). Detection and
characterization of translational research in cancer and cardio-
vascular medicine. Journal of Translational Medicine, 9, 57.
Jurgens, D., Kumar, S., Hoover, R., McFarland, D., & Jurafsky, D.
(2018). Measuring the evolution of a scientific field through cita-
tion frames. Transactions of the Association for Computational
Linguistica, 6, 391–406.
Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., &
Dyer, C. (2016). Neural architectures for named entity recognition.
Negli Atti del 2016 Conferenza del Nord America
Chapter of the Association for Computational Linguistics: Umano
Language Technologies (pag. 260–270).
Latour, B. (1976). Including citation counting in the system of actions
of scientific papers. Paper presented at the First Annual Meeting of
the Society for Social Studies of Science, 4–6 November, Cornell
Università.
Latour, B. (2011). Networks, societies, spheres: Reflections of an
actor-network theorist. International Journal of Communication,
5, 796–810.
Latour, B., & Woolgar, S. (1979). Laboratory life. The social construction
of scientific facts. Beverly Hills: Sage.
Latour, B., Jensen, P., Venturini, T., Grauwin, S., & Boullier, D. (2012).
The whole is always smaller than its parts—A digital test of Gabriel
Tarde’s monads. The British Journal of Sociology, 63(4), 590–615.
Legge, J. (1983). Enrôlement et contre-enrôlement: Les luttes pour la
publication d’un article scientifique. Social Science Information,
22, 237–251.
Li, J., Zhu, X., & Chen, J. Y. (2009). Building disease-specific drug-
protein connectivity maps from molecular interaction networks and
PubMed abstracts. PLOS Computational Biology, 5(7), e1000450.
Marres, N. (2017). Digital sociology. Cambridge (UK): Polity Press.
Menezes, T., & Roth, C. (2019). Semantic Hypergraphs. arXiv preprint,
arXiv:1908.10784.
Moody, J. (2004). The structure of a social science collaboration network:
Disciplinary cohesion from 1963 A 1999. American Sociological
Review, 69(2), 213–238.
Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J. P.
(2010). Community structure in time-dependent, multiscale, E
multiplex networks. Scienza, 328(5980), 876–878.
Nadeau, D., & Sekine, S. (2007). A survey of named entity recognition
and classification. Lingvisticæ Investigationes, 30(1), 3–26.
Palla, G., Barabási, UN. L., & Vicsek, T. (2007). Quantifying social
group evolution. Nature, 446(7136), 664.
Price, D. J. de Solla (1963). Little science, big science. New York:
Columbia University Press.
Price, D. J. de Solla (1965). Networks of scientific papers. Scienza,
149(3683), 510–515.
Price, D. J. de Solla (1975). Science since Babylon. Nuovo paradiso: Yale
Stampa universitaria.
Rabeharisoa, V., & Bourret, P. (2009). Staging and weighting evidence
in biomedicine: Comparing clinical practices in cancer genetics and
psychiatric genetics. Social Studies of Science, 39, 691–715.
Rheinberger, H. J. (2009). Recent science and its exploration: IL
case of molecular biology. Studies in History and Philosophy of
Biological and Biomedical Sciences, 40, 6–12.
Ribes, D., Hoffman, UN. S., Slota, C. S., & Bowker, G. C. (2019). IL
logic of domains. Social Studies of Science, 49, 281–309.
Rosvall, M., & Bergstrom, C. T. (2010). Mapping change in large
networks. PLOS ONE, 5(1), e8694.
Rotolo, D., Rafols, I., Hopkins, M. M., & Leydesdorff, l. (2017).
Strategic intelligence on emerging technologies. Scientometric
overlay mapping. Journal of the Association for Information
Science and Technology, 68, 214–233.
Shi, F., & Evans, J. (2019) Science and technology advance through
sorpresa. arXiv preprint, arXiv:1910.09370.
Shi, F., Foster, J. G., & Evans, J. UN. (2015). Weaving the fabric of
science: Dynamic network models of science’s unfolding structure.
Social Networks, 43, 73–85.
Simondon, G. (1958). Du mode d’existence des objets techniques.
Paris: Aubier.
Small, H. (1973). Co-citation in the scientific literature: A new measure
of the relationship between two documents. Journal of the American
Society for Information Science, 24(4), 265–269.
Small, H. (1999). Visualizing science by citation mapping.
Journal of the American Society for Information Science, 50(9),
799–813.
Tancoigne, E., Barbier, M., Cointet, J. P., & Richard, G. (2014). IL
place of agricultural sciences in the literature on ecosystem services.
Ecosystem Services, 10, 35–48.
Taramasco, C., Cointet, J. P., & Roth, C. (2010). Academic team for-
mation as evolving hypergraphs. Scientometrics, 85(3), 721–740.
Thévenot, l. (2006). L’action au pluriel. Sociologie des régimes
d’engagement. Paris: La Découverte.
Van den Besselaar, P., & Heimeriks, G. (2006). Mapping research
topics using word-reference co-occurrences: A method and an
exploratory case study. Scientometrics, 68(3), 377–393
Van Eck, N. J., & Waltman, l. (2007). VOS: A new method for
visualizing similarities between objects. In H.-J. Lenz & R. Decker
(Eds.). Advances in data analysis: Proceedings of the 30th Annual
Conference of the German Classification Society (pag. 299–306).
Heidelberg: Springer.
Van Eck, N. J., & Waltman, l. (2009). Software survey: VOSviewer,
a computer program for bibliometric mapping. Scientometrics,
84(2), 523–538.
Van Eck, N. J., & Waltman, l. (2011). Text mining and visualization
using VOSviewer. arXiv preprint, arXiv:1109.2058.
Venturini, T., Munk, A.K., & Jacomy M. (2019). Actor-network versus
network analysis versus digital networks. Are we talking about the
same networks? In J. Vertesi & D. Ribes (Eds.). digitalSTS. A field
guide for science & technology studies (pag. 510–523). Princeton:
Princeton University Press.
Watts, D. (2004). The “new” science of networks. Annual Review
of Sociology, 30(1), 243–270.
Watts, S. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-
world’ networks. Nature, 393, 440–442.
Weisz, G., Cambrosio, A., & Cointet, J. P. (2017). Mapping global
health: A network analysis of a heterogeneous publication
domain. BioSocieties, 12(4), 520–542.
Zitt, M., & Bassecoulard, E. (2006). Delineating complex scien-
tific fields by an hybrid lexical-citation method: An application
to nanosciences. Information Processing & Management, 42(6),
1513–1531.
l
D
o
w
N
o
UN
D
e
D
F
R
o
M
H
T
T
P
:
/
/
D
io
R
e
C
T
.
M
io
T
.
/
e
D
tu
q
S
S
/
UN
R
T
io
C
e
–
P
D
l
F
/
/
/
/
1
3
1
0
1
7
1
8
6
9
8
4
4
q
S
S
_
UN
_
0
0
0
5
5
P
D
/
.
F
B
sì
G
tu
e
S
T
T
o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3
Quantitative Science Studies
1024
