RESEARCH ARTICLE
Geography of scientific knowledge:
A proximity approach
Koen Frenken
Copernicus Institute of Sustainable Development, Utrecht University, The Netherlands
a n o p e n a c c e s s
j o u r n a l
Keywords: citation, controversy, diffusion, mobility, replication, tacit knowledge
Citation: Frenken, K. (2020). Geography
of scientific knowledge: A proximity
approach. Quantitative Science
Studies, 1(3), 1007–1016. https://doi.
org/10.1162/qss_a_00058
DOI:
https://doi.org/10.1162/qss_a_00058
Corresponding Author:
Koen Frenken
k.frenken@uu.nl
Handling Editors:
Loet Leydesdorff, Ismael Rafols,
and Staša Milojević
Copyright: © 2020 Koen Frenken.
Published under a Creative Commons
Attribution 4.0 International (CC BY 4.0)
license.
The MIT Press
ABSTRACT
Proximity among scientists in social, cognitive, and physical dimensions promotes the sharing
of tacit knowledge. Tacit knowledge helps scientists to understand the credibility of papers
they read and to use the results in subsequent research. Hence, given the proximity among
scientists in social, cognitive, and physical dimensions, one can predict patterns of diffusion in
science. However, for controversial knowledge claims to become replicated, one expects the
proximity between scientists itself to change as like-minded scientists relocate and create new
coalitions. Proximity can thus be used as a unifying concept for the study of scientific
knowledge diffusion as well as for the analysis of mobility of scientists.
1.
INTRODUCTION
It is quite common to view scientific knowledge as essentially “placeless” (Livingstone, 2003). The
principle of replication in science holds that a knowledge claim can be said to be true if this claim
is repeatedly confirmed by independent replication studies. This view was challenged by many
cases showing that replication is very difficult in practice (Begley & Ioannidis, 2015; Collins,
1985). In particular, for scientists it is hard to establish whether two studies yield the exact same
results or not. Furthermore, many studies are never replicated at all, but their results nevertheless
can become widely accepted as truths in the scientific community. This shows that what is being
“replicated” in many instances is not the research as such, but only the knowledge claim based on
the initial research (Latour, 1987).
One can formulate, as the central research question for a geography of scientific knowledge,
under what conditions a knowledge claim originating from one place becomes accepted as
scientific in other places (Shapin, 1998). This formulation allows one to shed a new light on scientific
knowledge production by bringing in concepts and methodologies from geography. Since research,
by nature, is geographically localized in specific places–be they offices, labs, or field sites—the
question of how knowledge claims become widely accepted outside the places of origin can be
understood as a question of spatial diffusion and the role of distance herein. In developing a
theoretical framework, I will make use of proximity (Bellet, Colletis, et al., 1993; Boschma, 2005),
as the mirror concept of distance, to disentangle the various types of relationships between
scientists—cognitive, social, and physical—that support the diffusion of knowledge claims.
2. GEOGRAPHY, SCIENCE, AND SCIENTIFIC KNOWLEDGE
Rather than posing the philosophical question of which empirical knowledge can be said to be
scientifically true or untrue, sociologists of scientific knowledge analyze the conditions under
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Geography of scientific knowledge
which a knowledge claim becomes established as scientific among scientists (Collins, 1985;
Gilbert, 1976; Latour, 1987; Shapin, 1984). The sociology of scientific knowledge is not only
different from the philosophy of science, but also departs from the Mertonian program in the
sociology of science (Merton, 1973). This latter tradition investigates the institutions governing
scientific activity rather than the conditions under which a knowledge claim becomes accepted
within science.
Analogous to the distinction between sociology of scientific knowledge and the classic sociology
of science, I use the phrase geography of scientific knowledge for the study of how knowledge
claims become replicated across different places, while geography of science covers the larger set
of questions regarding the local conditions and shaping of scientific knowledge and spatial diffusion
of scientific practices and institutions (Barnes, 2001; Finnegan, 2008; Livingstone, 2003;
Meusburger, Livingstone, & Jöns, 2010; Naylor, 2005; Powell, 2007). Thus, like the sociology of
scientific knowledge, the geography of scientific knowledge focuses on the process rendering
knowledge claims scientific.
To put forward a coherent framework for the study of the geography of scientific knowledge,
the scope of the present paper is deliberately limited. First, I focus only on how claims are being
replicated among scientists. Thus, I am not concerned here with how knowledge claims become
replicated in society at large (Latour, 1987; Porter, 1995), nor do I go into the question of why
certain knowledge claims originate in some places rather than others (Heimeriks & Boschma,
2014; Nomaler, Frenken, & Heimeriks, 2014; Whatmore, 2009). Second, the framework is an
analytical one, which aims to serve as a framework for science studies.
3. ON REPLICATION IN SCIENCE
In the early stages of what is generally associated with modern experimental science, fellow
scientists were often invited to witness an experiment in person (Shapin, 1994). The copresence
of individuals was of importance in their role as witnesses to establish a consensus about what
exactly was being observed and how these observations were to be interpreted. Progressively,
copresence became less common (though it still plays a role in team research or in contact with
the media). Witnessing has instead become organized through codification in written reports that
are evaluated by peers before and after publication. Empirical knowledge is abstracted from the
context of discovery by codifying the knowledge claim in written form so that it can travel (Latour,
1987; Shapin, 1984, p. 491). Without codification of a knowledge claim in a written report, the
production of knowledge claims would be severely constrained by space and time due to the need
for physical copresence. The credibility of a knowledge claim would then depend solely on the
testimonies of those who have witnessed the experiment.
Empirical knowledge becomes established as scientific knowledge once the empirical results of
a study are accepted as credible by others who were not copresent at the research site. To establish
credibility, a written report should describe not only the actual event but also the laboratory
conditions (objects, physical conditions, equipment, protocols, methodologies, etc.). Careful
reporting of laboratory conditions should allow fellow scientists to replicate the experiment
independently at different sites. Subsequent replications of an experiment by fellow scientists, if
successful, lead to the accumulation of confirmations of the original claim. It is common to view
the process of successful replications of experiments at different sites as evidence that the original
knowledge claim is universally true. Accordingly, scientific knowledge is commonly considered
as “placeless” (Livingstone, 2003) in the sense that claims that have been proven replicable in
different places hold independently of the private observations of scientists who have been
involved in the process of successive replications.
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Geography of scientific knowledge
However, the set of instructions as codified in a scientific paper are generally insufficient for
fellow scientists to be able to replicate an experiment. The codified instructions have to be
complemented with the relevant tacit knowledge and information on contextual conditions for
scientists to be able to replicate the underlying experiment (Balconi, Pozzali, & Viale, 2007;
Collins, 1985; Van Bavel, Mende-Siedlecki, et al., 2016). Codified information cannot be
exchanged in a fully unambiguous manner, as the receiver of codified information still requires
complementary tacit knowledge as “interpretative skills” to interpret the information content as
meaningful in a particular material context (Balconi et al., 2007, p. 836 and p. 842; cf. Nelson,
2003, p. 911).
Given that replication studies involve both tacit and codified knowledge, and the tacit elements
of knowledge cannot be transferred perfectly among scientists, scientists can never be fully certain
that they have replicated a study perfectly. If, for example, a laboratory replicates an experiment
and comes up with (slightly) different results, the scientists involved cannot ascertain whether the
divergence in results means that the original claim must be rejected or whether the replication
experiment has been carried out in the wrong way, that is, in a different way than was reported
in the original claim1. In fact, replication studies seldom underlie scientific agreement, as many
results become accepted without any other researcher ever attempting to replicate the studies
underlying the results (Begley & Ioannidis, 2015).
Following this reasoning, Shapin (1984) argued that the establishment of an empirical knowl-
edge claim as scientific is fundamentally driven by the perceived replicability of the experiment,
and not necessarily by actual replications of the experiment. The codification of experimental
results in a report can thus be considered as a “literary technology of virtual witnessing”
(Shapin, 1984); that is, a means to convince the reader that the experiment described could be
replicated in different places and at different times if one were to follow the description of the
laboratory conditions as written down in the report. The disclosure of the laboratory conditions
is essential to suggest that the report is trustworthy and the claim is credible if any scientist were to
decide to attempt to replicate the experiment. It is for this reason that the description of laboratory
conditions in itself is often sufficient for a knowledge claim to become established as scientific.
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A replication study and the replication of a knowledge claim are thus two very different
processes. In the case of the replication of a knowledge claim, some of the implications of the
experiment are replicated as an input to carry out a new research project, without the study itself
being replicated. Previous knowledge claims guide the production of future knowledge claims
through their use in assumptions, methodologies, instruments, or interpretations (Dasgupta &
David, 1994, p. 500). Research projects build on previous research by extending it to new
domains, not by attempting to perfectly replicate past experiments. In doing so, previous claims
are mobilized, and thus replicated, to support a new claim (Latour, 1987).
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The replication process of knowledge claims becomes visible in codified form through refer-
encing (Gilbert, 1977). The study of the diffusion of knowledge can in principle be done in a
1 The fundamental problem in replication experiments lies in what Collins called “experimenters’ regress,” which
“arises because the skill-like nature of experimentation means that the competence of experimenters and the
integrity of experiments can only be ascertained by examining results, but the appropriate results can only be
known from competently performed experiments, and so forth. Other ways of testing for the competence and
integrity of experiments, such as tests of tests, turn out to need ‘tests of tests of tests’ and so on” (Collins, 1985,
p. 130). In short, to establish whether equipment is properly working, one can carry out an experiment with a
“known” outcome, but this outcome has become established in other experiments using the equipment at
hand. Alternatively, one may test the working of equipment using test equipment, but this moves the problem
one level up (“tests of tests of tests”). Laboratories, then, can be understood as an infrastructure to “construct
predictability,” which is not to be confused with perfect replication (Nightingale, 2004).
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Geography of scientific knowledge
purely scientometric manner, by studying the citation links between scientific articles. And, as
a basic theory, one can adopt an evolutionary lens to understand the dynamics of scientific
knowledge production: Often-cited claims get selected while seldom-cited claims get forgotten
(Leydesdorff, 1998; Martinelli & Nomaler, 2014). The geography of scientific knowledge
production, then, would focus on the spatial diffusion of knowledge claims.
4. PROXIMITY
I pose as the central research question for a geography of scientific knowledge: What determines
that a knowledge claim originating from one place becomes accepted as scientific in other places?
Following Shapin (1995, p. 261), the credibility of a knowledge claim depends on the relationship
between the one(s) putting forward the claim (“claimant”), and the one judging its credibility
(“evaluator”). As argued, previous knowledge claims guide the production of new knowledge
claims through their use in assumptions, methodologies, instruments, or interpretations. The pro-
cess of replication of a knowledge claim thus takes on the form of the use of a claim as a resource,
or input, to establish a new claim. Since knowledge claims are replicated in subsequent knowl-
edge claims in these manners, the chance of a claim being replicated will depend not only on its
credibility but also on its usefulness in guiding new research projects. Thus, for a claim to be rep-
licated as an input in a subsequent empirical research context, evaluators should consider the
claim to be both credible and useful2. The question becomes how an evaluator establishes the
credibility and usefulness of a claimant’s knowledge claim.
I will approach this question by focusing on the relationship between the claimant and the
evaluator. To characterize the relationship between claimant and evaluator, I will make use of
notions of proximity as developed in economic geography (Balland, Boschma, & Frenken, 2015;
Bellet et al., 1993; Boschma, 2005; Rallet, 1993; Torre & Rallet, 2005). Proximity simply denotes
the inverse of the distance between two scientists. Proximity can refer to physical proximity in the
literal sense of physical distance separating two scientists (which Rallet and Torre tend to call
geographical proximity), or it can refer to other forms of proximity. The thesis that is developed in
detail below holds that the more distant the claimant and evaluator are in physical and nonphysical
spaces, the less probable the claimant’s knowledge claim is being replicated by the evaluator.
To be able to judge a claim, cognitive proximity (Nooteboom, 1999) seems to be by far the most
important factor. Cognitive proximity in a narrow sense can be defined as the extent to which two
scientists share the code of communication that is used in a particular disciplinary context.
Following this narrow definition, cognitive proximity allows an evaluator to decode the written
report containing the knowledge claim and to assess its credibility and usefulness. Cognitive
proximity, in the sense just defined, is consistent with the idea that knowledge can be transmitted
as information as long as the sender and receiver use the same code of communication to code and
decode the message (Cowan, David, & Foray, 2000). The idea that knowledge can be exchanged
as codified information provided that two agents share the same codebook has been criticized on
several grounds (Balconi et al., 2007; Nelson, 2003; Nightingale, 2003). A codebook can be seen
as a language that must be shared between agents to allow for verbal and written communication.
2 The two main replication criteria are also visible in the almost standard setup of scientific papers. Usefulness
is mainly dealt with in the review section, showing how it builds on previous research. Credibility is mainly
dealt with in the methodology section, where the procedures are specified that should be followed in an
attempt to replicate the experiment itself. It is often during the review process that the “right” level of
credibility and usefulness is negotiated with the claimant having to give in by qualifying its claims as being
credible only under particular assumptions or particular contexts, and, therefore, being relevant only to a
small domain of scientific inquiry (Myers, 1985). Ultimately, of course, the findings may still be judged as
being more or less relevant and more or less credible than the author has stated in the published version.
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Geography of scientific knowledge
However, because languages cannot be fully formalized, the meaning of symbols referring to
empirical objects remains to some extent tacit. A language cannot be taught, but must be learned
in localized practices by participating in material contexts in which a language is used in a specific
way. In this context, Balconi et al. (2007, pp. 840–843) distinguish between tacit knowledge of the
physical (“skill-like”) type and of the cognitive type. The latter type is often overlooked, but may be
more important in understanding the replication process of knowledge claims. What is more, in
the codification process of tacit knowledge of the physical type (e.g., into computer algorithms),
new tacit knowledge of the cognitive type is being created concerning the appropriate use of
codified information in particular research contexts3.
Generally, the evaluator cannot fully judge the credibility and the usefulness of a claim from a
text alone. As text length is limited, many details will be left out. What is more, the tacit knowledge
involved in the experiment is not transmitted with the text. Thus, an evaluator would benefit from
having similar tacit knowledge as was involved in carrying out the experiment. These “intellectual
skills” (Balconi et al., 2007, p. 842) allow an evaluator to “read between the lines” and better
understand how the experiment was carried out, whether the observational reports are trustworthy,
and how likely the experiment is to be replicated with a high degree of precision. Thus, cognitive
proximity in a broader sense can be taken to mean the extent to which claimant and evaluator share
both codified and tacit knowledge regarding the experimental context in question. It is cognitive
proximity that allows scientists to judge a knowledge claim without any face-to-face interaction
between claimant and evaluator; cognitive proximity is what makes Shapin’s “virtual witnessing”
possible. From reading a text, a “competent” evaluator can judge its credibility and usefulness4.
Obviously, few people are cognitively close; typically, high levels of cognitive proximity will
only be found in small communities that make up highly specialized subdisciplines that repro-
duce themselves by training their own successors. This explains why most claims in science are
assessed on credibility and usefulness only within these small communities—often connected to
a journal—and without any interference from actors outside these communities. Actors outside
these communities, including fellow scientists and (a large part of ) the public, derive the cred-
ibility and usefulness of claims indirectly from the peer assessment carried out within these small
subcommunities (Shapin, 1995, pp. 269–271).
The assessment of a claim in terms of credibility and usefulness is often carried out without any
interaction between claimant and evaluator. In the role of peer reviewer, or reader, the evaluator will
judge a report without feedback from the claimant. As long as the claimant and evaluator have
sufficient knowledge in common, claims can be assessed at a distance. Yet, the subsequent
replication of a knowledge claim as an input in new experiments typically necessitates, or at least
benefits from, face-to-face interaction to transfer tacit knowledge, instruments, materials, further
information, and so on. It is for this reason that some form of interaction between claimant and
evaluator often precedes the replication of a knowledge claim in a subsequent claim. This can vary
from personal correspondence or small talk to more intensive forms of interaction, including site visits,
temporary exchange of personnel, and collaborative research projects. All these forms of interaction
are examples of close face-to-face interaction rendering these interactions fundamentally different
from written communication or oral presentation involving a one-to-many form of interaction.
3 This also means that even attempts by scientists to reproduce results from the raw data collected by others (as
opposed to replicating) may be hard, as tacit knowledge may still be involved in knowing how to use software,
follow data entry procedures, and collect results. What is more, in some cases, the software may operate differ-
ently depending on the hardware used.
4 Indeed, anonymous peer review is based on such judgments without any interaction between claimant and
evaluator (even if the editor generally, but not necessarily, acts as a mediator interacting with both parties).
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Geography of scientific knowledge
However, colocation is not a sufficient condition for tacit knowledge sharing to take place
(Boschma, 2005). A key question that follows is under what conditions scientists are willing to
share tacit knowledge, information, and other resources5. Although scientists’ main incentive is
to see their knowledge claims being replicated (Hull, 1988), they have little incentive to see their
resources being replicated, because sharing resources allows other researchers to pursue the same
research lines and to pre-empt future publications (Dasgupta & David, 1994). Since reward in
science is allocated on the basis of priority in research findings (whatever the exact ways in which
priority is established), the incentive to share resources is limited. What is more, sharing resources
is a costly affair, especially for tacit knowledge that requires teaching and on-the-job training,
while compensation schemes for sharing activities are rare.
An important reason why scientists nevertheless share resources quite regularly is the risk of
reputational loss. Within the subcommunities in which scientists are operating, those unwilling
to share tacit knowledge will run the risk that third parties will no longer share resources with them
once they are notified about their unwillingness (Dasgupta & David, 1994, p. 504). In this respect,
the concept of social proximity can be used to explain the willingness of scientist A to share
resources with scientist B. Social proximity, following Granovetter (1973), may refer to the number
of fellow scientists that A and B know in common6. The higher this number, the higher the repu-
tational consequences once word gets out that A does not behave cooperatively, given that B will
warn those that A and B know in common about A’s noncooperative behavior. Thus, the higher
the social proximity between claimant and evaluator, the higher the willingness of the claimant to
share tacit knowledge and other resources with the evaluator, the higher the probability that the
evaluator will replicate the claim in subsequent claims. As for cognitive proximity, social proximity
is typically high in small subcommunities in which the members frequently meet, carry out peer-
review, and engage in collaborative research projects. Given the specialized nature of scientific
knowledge production, cognitive and social proximity will tend to be highly correlated (Breschi &
Lissoni, 2009).
5. CONTROVERSIES
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Although the proposed focus of the geography of scientific knowledge is on the replication of
knowledge claims rather than of underlying laboratory experiments, this focus does not imply that
replication studies do not influence the dynamics of scientific knowledge production. One may
ask under what conditions scientists have an incentive to engage in replications of experiments.
Most attempts to replicate an experiment involve a costly investment. The returns on such an
investment depend on the status of the knowledge claim that resulted from the experiment in
question. When a knowledge claim is considered to be credible and useful for scientists’ further
research, there is little interest in devoting resources to replication attempts. If such an attempt fails
to replicate the earlier reported findings, fellow scientists will generally believe that the replication
experiment has been carried out incorrectly. And, if the replication experiment succeeds in
replicate the earlier reported findings, it will be regarded as replicating “the obvious.” In both
cases, the results of the replication experiment may be deemed unworthy of publication in
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5 A related question is under what conditions scientists want to share material objects relevant for research.
There is evidence that scientists are reluctant to share materials if competition is fierce and the cost of sharing
is high (Walsh, Cohen, & Cho, 2007). Exchange of materials differs, however, from the sharing of tacit
knowledge, because the former does not require face-to-face interaction per se.
6 Social proximity as defined here follows the older notion of tie strength proposed by Granovetter as the
degree of overlap of two individuals’ friendship networks (Granovetter, 1973, p. 1362). Note that the mea-
sure of social proximity proposed here differs from the measure of social proximity as the shortest geodesic
distance in the coauthor network (Breschi & Lissoni, 2009).
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scientific journals. Given that investments in replications of credible knowledge claims have low
returns, whatever the outcome of the replication experiments, few scientists engage in such
research (Collins, 1985).
By contrast, if a knowledge claim is considered controversial, meaning that its acceptance would
contradict many previous claims on which scientists have built their research in the past, scientists
will have an incentive to engage in replication (Collins, 1985, p. 19). In such contexts, replication
exercises have the explicit goal to replicate an experiment so as to confirm or disconfirm a claim.
Given the problem of “experimenters’ regress” (see footnote 1), there cannot be a fully agreed meth-
odology that distinguishes between correct and incorrect experiments. What is more, controversies
often arise with the advent of new instrumentalities rather than from theoretical advances (de Solla
Price, 1984), which implies that both technological and methodological uncertainties may exist
regarding the assessment of experimental evidence. That is why the outcome of controversies is
contingent, at least to some degree, upon the entrepreneurial ability of each participant to find
the resources required to improve experiments technologically as well as methodologically, and
upon the rhetorical ability to convince fellow scientists of the methodological soundness of their
own experiment (Latour, 1987). If, over time, no agreed methodology emerges, the different posi-
tions can lead a research community to fragment into two subcommunities characterized by
their own methodology (Hull, 1988). Alternatively, agreement may emerge on one particular
position, or a synthesis between different positions, and the community remains intact.
In science and technology studies, scientific controversy constitutes a longstanding sepa-
rate topic of research, including studies looking at the formation of new coalitions supporting a
new paradigm (Jasanoff, 2020; Kuhn, 1962). Similarly, within our proximity framework, the
distinction between controversial and uncontroversial knowledge claims has important con-
sequences. Whereas collaborative behavior regarding the sharing of resources can be ex-
plained from cognitive, social, and physical proximity in the case of uncontroversial
knowledge claims, this explanatory scheme no longer works for controversial knowledge
claims. Characteristic of controversy is that there is no agreement on the conditions under
which replication attempts can be considered true replication, due to the problem of experi-
menter’s regress (Collins, 1985). One can thus expect that, even more so than in uncontrover-
sial contexts, scientists may be unwilling to share tacit knowledge and other resources, which
otherwise are crucial to arriving at a common understanding of divergent results in replication
attempts. The radical uncertainty regarding the outcome of a controversy, which generally
lasts for several years, forces each scientist to “take sides.” To participate in the controversy,
a scientist must allocate time and resources between the established, mainstream research pro-
gram and the emerging, controversial research program.
Following the spatial analogy implicit in the proximity concept, it can be argued that taking
sides can be analyzed as a form of mobility. Following the proximity framework, one can distin-
guish between cognitive mobility (e.g., moving from the established research trajectory to a com-
peting research trajectory), social mobility (leaving the established community to join a new
subcommunity), and mobility in the literal physical sense (moving between physical sites).
Mobility in the physical sense is crucial, because the establishment of cognitive and social prox-
imity often requires face-to-face interaction between likeminded scientists. That is, colocation
enables the creation of new social networks and joint cognitive investments in emerging research
programs, including manuals, software, equipment, and, of course, laboratories. Ideally, mobility
would lead to permanent colocation of those who wish to join an emerging research trajectory
so as to be able to work together on-site on a permanent basis.
The mobility dynamics of scientists along cognitive, social, and physical dimensions may
greatly affect the dynamics of scientific knowledge production and the establishment of new
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research trajectories. The more people join a particular research trajectory, the more resources
become available to produce, and the more likely that knowledge claims will be replicated in
subsequent research projects7. The reason why critical mass matters is that experiments that are
intended to confirm a claim are typically carried out differently than experiments intended to
disprove a claim, and experiments done by proponents will more often find confirming evidence
and experiments done by opponents will more often find the opposite (Collins, 1985). What is
more, there exists a publication bias in that experiments with positive results are more easily accept-
ed by scientific journals than experiments yielding insignificant or negative results (for a review, see
Dwan, Altman, et al., 2008). This is not to say that empirical evidence “in itself” does not play a role.
On the contrary, the accumulation of empirical evidence is generally decisive in settling controver-
sies. Yet, this process cannot be understood solely as a process of inductive reasoning, as scientists
do not share the epistemological foundation required to agree on what counts as a replication,
which would be necessary to aggregate such evidence.
6. CONCLUDING REMARKS
The geography of scientific knowledge production is understood as a process through which
locally produced knowledge claims become accepted as scientific elsewhere. Even if scien-
tists, in principle, can replicate each other’s knowledge claims just by referencing, face-to-face
interaction remains important in the replication of knowledge claims to transfer the supporting
tacit knowledge. By sharing tacit knowledge through face-to-face interaction, scientists are
better able to judge the credibility and usefulness of a claim and, thereby, to build upon each
other’s findings in a cumulative manner.
The proximity framework provides a theoretical explanation of the empirical observation that
the probability of citation between two articles goes up with physical proximity (Frenken,
Hardeman, & Hoekman, 2009). Recent studies, however, found that the effect of physical prox-
imity is quite small once cognitive proximity is properly controlled for (Head, Li, & Minondo,
2019; Wuestman, Hoekman, & Frenken, 2019), which also indicates the positive correlation
between physical and cognitive proximity. Furthermore, a study showed that social proximity
between countries also plays a role in explaining citation patterns, even after controlling for phys-
ical proximity (Hellmanzik & Kuld, 2018). These initial findings call for more refined research on
citation patterns based on different notions of proximity, opening up a new, theoretically informed
research program in the (quantitative) studies of science, which can also leverage past research on
proximity in innovation studies (Balland et al., 2015). Here, the geography of scientific knowledge
production can build on a longer tradition in science studies on the measurement of cognitive
proximity using co-occurrence data (Van Eck & Waltman, 2009) and can find inspiration in more
recent attempts to combine physical, cognitive, and social proximity measures in a single research
design (Baccini, Barabesi, et al., 2020; Head et al., 2019; Wuestman et al., 2019).
In all, the proximity framework provides an analytical framework in which different theoretical
traditions can be combined and translated into a single research design, here, to explain the
replication logic of knowledge claims. As such, it speaks to repeated calls for more theorization
in the field of scientometrics (Cronin, 1981; Leydesdorff, 1998; Fortunato, Bergstrom, et al., 2018).
In the present contribution, I have drawn primarily on theories in economic geography and
science and technology studies that stress the role of tacit knowledge sharing among scientists
as supportive of the understanding and acceptance of new claims. In doing so, I have limited my-
self to reflections about the role of, and interplay between, three forms of proximity: physical, cog-
nitive, and social.
7 Also known as increasing returns to adoption (Arthur, 1989).
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A further extension can be envisaged by including more proximity dimensions, in particular the
role of institutional proximity. A lack of institutional proximity would characterize the relations
between scientists and other actors in society (Ponds, Van Oort, & Frenken, 2007). This would
extend the proximity framework presented here with its sole focus on knowledge diffusion
between scientists operating under the same academic institutions to knowledge diffusion be-
tween actors operating under different institutions, including firms, governments, and NGOs.
ACKNOWLEDGMENT
The paper builds on a longer previous version, which has appeared as a working paper
(Frenken, 2010).
COMPETING INTERESTS
The author has no competing interests.
FUNDING INFORMATION
The author received funding under the NWO Vici program, number 453-14-014.
DATA AVAILABILITY
Not applicable.
REFERENCES
Arthur, W. B. (1989). Competing technologies, increasing returns, and
lock-in by historical events. Economic Journal, 99(394), 116–131.
Baccini, A., Barabesi, L., Khelfaoui, M., & Gingras, Y. (2020).
Intellectual and social similarity among scholarly journals: An
exploratory comparison of the networks of editors, authors and
co-citations. Quantitative Science Studies, 1(1), 277–289.
Balconi, M., Pozzali, A., & Viale, R. (2007). The “codification debate”
revisited: A conceptual framework to analyze the role of tacit
knowledge in economics. Industrial and Corporate Change, 16(5),
823–849.
Balland, P. A., Boschma, R., & Frenken, K. (2015). Proximity and
innovation: From statics to dynamics. Regional Studies, 49(6),
907–920.
Barnes, T. J. (2001). “In the beginning was economic geography”:
A science studies approach to disciplinary history. Progress in
Human Geography, 25(4), 455–478.
Begley, C. G., & Ioannidis, J. P. (2015). Reproducibility in science.
Improving the standard for basic and preclinical research.
Circulation Research, 116(1), 116–126.
Bellet, M., Colletis, G., & Lung, Y. (1993). Économie des proximités.
Revue d’Economie Régionale et Urbaine, 3, 357–606.
Boschma, R. (2005). Proximity and innovation. A critical assessment.
Regional Studies, 39(1), 61–74.
Breschi, S., & Lissoni, F. (2009). Mobility of skilled workers and co-
invention networks: An anatomy of localized knowledge flows.
Journal of Economic Geography, 9(4), 439–468.
Collins, H. M. (1985). Changing order: Replication and induction in
scientific practice. London: Sage.
Cowan, R., David, P. A., & Foray, D. (2000). The explicit economics of
knowledge codification and tacitness. Industrial and Corporate
Change, 9(2), 211–253.
Cronin, B. (1981). The need for a theory of citation. Journal of
Documentation, 37(1), 16–24.
Dasgupta, P., & David, P. A. (1994). Toward a new economics of
science. Research Policy, 23(5), 487–521.
de Solla Price, D. J. (1984). The science/technology relationship, the
craft of experimental science, and policy for the improvement of
high technology innovation. Research Policy, 13(1), 3–20.
Dwan, K., Altman, D. G., Arnaiz, J. A., Bloom, J., Chan, A.-W., …
Williamson, P. R. (2008). Systematic review of the empirical evidence
of study publication bias and outcome reporting bias. PLOS ONE,
3(8), e3081.
Finnegan, D. A. (2008). The spatial turn: Geographical approaches
in the history of science. Journal of the History of Biology, 41(2),
369–388.
Fortunato, S., Bergstrom, C. T., Börner, K., Evans, J. A., Helbing, D., …
Barabási, A.-L. (2018). Science of science. Science, 359(6379),
eaao0185.
Frenken, K. (2010). The geography of scientific knowledge: A prox-
imity approach. ECIS Working paper, 10-01, Eindhoven
University of Technology, The Netherlands.
Frenken, K., Hardeman, S., & Hoekman, J. (2009). Spatial sciento-
metrics: Towards a cumulative research program. Journal of
Informetrics, 3(3), 222–232.
Gilbert, G. N. (1976). The transformation of research findings into
scientific knowledge. Social Studies of Science, 6(3–4), 281–306.
Gilbert, G. N. (1977). Referencing as persuasion. Social Studies of
Science, 7(1), 113–122.
Granovetter, M. S. (1973). The strength of weak ties. American
Journal of Sociology, 78(6), 1360–1380.
Head, K., Li, Y. A., & Minondo, A. (2019). Geography, ties, and
knowledge flows: Evidence from citations in mathematics.
Review of Economics and Statistics, 101(4), 713–727.
Heimeriks, G., & Boschma, R. (2014). The path- and place-dependent
nature of scientific knowledge production in biotech 1986–2008.
Journal of Economic Geography, 14, 339–364.
Quantitative Science Studies
1015
l
D
o
w
n
o
a
d
e
d
f
r
o
m
h
t
t
p
:
/
/
d
i
r
e
c
t
.
m
i
t
.
/
e
d
u
q
s
s
/
a
r
t
i
c
e
–
p
d
l
f
/
/
/
/
1
3
1
0
0
7
1
8
6
9
8
4
2
q
s
s
_
a
_
0
0
0
5
8
p
d
.
/
f
b
y
g
u
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Geography of scientific knowledge
Hellmanzik, C., & Kuld, L. (2018). No place like home—home bias
in the dissemination of economic research articles. Mimeo, TU
Dortmund, February 15.
Hull, D. L. (1988). Science as a process: An evolutionary account
of the social and conceptual development of science. Chicago:
University of Chicago Press.
Jasanoff, S. (2020). Controversy studies. In: G. Ritzer (Ed.), The
Blackwell Encyclopedia of Sociology. Oxford: Blackwell.
Kuhn, T. S. (1962). The structure of scientific revolutions. Chicago:
University of Chicago Press.
Latour, B. (1987). Science in action: How to follow scientists and
engineers through society. Cambridge, MA: Harvard University Press.
Leydesdorff, L. (1998). Theories of citation? Scientometrics, 43(1), 5–25.
Livingstone, D. M. (2003). Putting science in its place: Geographies
of scientific knowledge. Chicago: University of Chicago Press.
Martinelli A., & Nomaler, Ö. (2014). Measuring knowledge persis-
tence: A genetic approach to patent citation network. Journal of
Evolutionary Economics, 24(3), 623–652.
Merton, R. K. (1973). The sociology of science: Theoretical and
empirical investigations. Chicago: University of Chicago Press.
Meusburger, P., Livingstone, D., & Jöns, H. (Eds.) (2010). Geographies
of science. Berlin: Springer.
Myers, G. (1985). Texts as knowledge claims: The social construction
of two biology articles. Social Studies of Science, 15(4), 593–630.
Naylor, S. (2005). Introduction: Historical geographies of science—
places, contexts, cartographies. British Journal for the History of
Science, 38(1), 1–12.
Nelson, R. R. (2003). On the uneven evolution of know-how.
Research Policy, 32(6), 909–922.
Nightingale, P. (2003). If Nelson and Winter are only half right
about tacit knowledge, which half? A Searlean critique of “cod-
ification.” Industrial and Corporate Change, 12(2), 149–183.
Nightingale, P. (2004). Technological capabilities, invisible infrastruc-
ture and the un-social construction of predictability: The overlooked
fixed costs of useful research. Research Policy, 33(9), 1259–1284.
Nomaler, Ö., Frenken, K., & Heimeriks, G. (2014). On scaling of
scientific knowledge production in U.S. metropolitan areas.
PLOS ONE, 9(10), e110805.
Nooteboom, B. (1999). Inter-firm alliances: Analysis and design.
London: Routledge.
Ponds, R., Van Oort, F., & Frenken, K. (2007). The geographical
and institutional proximity of research collaboration. Papers in
Regional Science, 86(3), 423–443.
Porter, T. M. (1995). Trust in numbers: The pursuit of objectivity in
science and public life. Princeton: Princeton University Press.
Powell, R. C. (2007). Geographies of science: Histories, localities,
practices, futures. Progress in Human Geography, 31(3), 309–329.
Rallet, A. (1993). Choix de proximité et processus d’innovation
technologique. Revue d’Economie Régionale et Urbaine, 3(special
issue “Economie de Proximités”)(3), 365–386.
Shapin, S. (1984). Pump and circumstance: Robert Boyle’s literary
technology. Social Studies of Science, 14(4), 481–520.
Shapin, S. (1994). A social history of truth: Civility and science in
seventeenth century England. Chicago: University of Chicago Press.
Shapin, S. (1995). Cordelia’s love: Credibility and the social studies
of science. Perspectives on Science, 3(3), 255–275.
Shapin, S. (1998). Placing the view from nowhere: Historical and
sociological problems in the location of science. Transactions
of the Institute of British Geographers, 23(1), 5–12.
Torre, A., & Rallet, A. (2005). Proximity and localization. Regional
Studies, 39(1), 47–60.
Van Bavel, J. J., Mende-Siedlecki, P., Brady, W. J., & Reinero, D. A.
(2016). Context sensitivity in scientific reproducibility. Proceedings
of the National Academy of Sciences, 113(23), 6454–6459.
Van Eck, N. J., & Waltman, L. (2009). How to normalize cooccur-
rence data? An analysis of some well-known similarity measures.
Journal of the American Society for Information Science and
Technology, 60(8), 1635–1651.
Walsh, J. P., Cohen, W. M., & Cho, C. (2007). Where excludability
matters: Material versus intellectual property in academic bio-
medical research. Research Policy, 36(8), 1184–1203.
Whatmore, S. J. (2009). Mapping knowledge controversies: Science,
democracy and the redistribution of expertise. Progress in Human
Geography, 33(5), 587–598.
Wuestman, M. L., Hoekman, J., & Frenken, K. (2019). The geography
of scientific citations. Research Policy, 48(7), 1771–1780.
Quantitative Science Studies
1016
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