RESEARCH ARTICLE - IA de Investigación especializada en el MIT

ARTÍCULO DE INVESTIGACIÓN

Intellectual and social similarity among
scholarly journals: An exploratory
comparison of the networks of editors,
authors and co-citations

un acceso abierto

diario

Alberto Baccini1

, Lucio Barabesi1

, Mahdi Khelfaoui2, and Yves Gingras2

1Department of Economics and Statistics, Università degli Studi di Siena, Siena, Italia
2Université du Quebec, Montréal, Canada

Citación: Baccini, A., Barabesi, l.,
Khelfaoui, METRO., & 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. https://
doi.org/10.1162/qss_a_00006

DOI:
https://doi.org/10.1162/qss_a_00006

Recibió: 28 Junio 2019
Aceptado: 21 Agosto 2019

Autor correspondiente:
Alberto Baccini
alberto.baccini@unisi.it

Editor de manejo:
Juego Waltman

Derechos de autor: © 2019 Alberto Baccini,
Lucio Barabesi, Mahdi Khelfaoui, y
Yves Gingras. Published under a
Creative Commons Attribution 4.0
Internacional (CC POR 4.0) licencia.

La prensa del MIT

Palabras clave: interlocking editorship network, gatekeeper, interlocking authorship network, co-citation
network, generalized distance correlation, communities in network

ABSTRACTO

This paper explores, by using suitable quantitative techniques, to what extent the intellectual
proximity among scholarly journals is also proximity in terms of social communities gathered
around the journals. Three fields are considered: Estadísticas, economics and information and
library sciences. Co-citation networks represent intellectual proximity among journals. El
academic communities around the journals are represented by considering the networks of
journals generated by authors writing in more than one journal (interlocking authorship: IA),
and the networks generated by scholars sitting on the editorial board of more than one
journal (interlocking editorship: IE). Dissimilarity matrices are considered to compare the
whole structure of the networks. The CC, IE, and IA networks appear to be correlated for the
three fields. The strongest correlation is between CC and IA for the three fields. Lower and
similar correlations are obtained for CC and IE, and for IE and IA. The CC, IE, and IA networks
are then partitioned in communities. Information and library sciences is the field in which
communities are more easily detectable, whereas the most difficult field is economics. El
degrees of association among the detected communities show that they are not independent. Para
all the fields, the strongest association is between CC and IA networks; the minimum level of
association is between IE and CC. En general, these results indicate that intellectual proximity is
also proximity among authors and among editors of the journals. De este modo, the three maps of
editorial power, intellectual proximity, and authors communities tell similar stories.

INTRODUCCIÓN

The main objects analyzed in this paper are scholarly journals and communities gathered
around them. Scholarly journals have grown in relevance as outlets for communicating re-
search results in the social sciences and humanities (Kulczycki et al., 2018), following a trend
that began in the natural sciences a century earlier (Csiszar, 2018). Over the last two decades,
in the context of the publish-or-perish environment, where the academic careers of scholars
depend more and more on the ”quality” of the journals in which they have published their
artículos, journals have gained a new importance as brands (Heckman & Moktan, 2018). Es
therefore hardly surprising that the interest of scientometric scholars for journals is mainly fo-
cused on the building of indicators, such as the impact factor, to be used for evaluative

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Intellectual and social similarity among scholarly journals

purposes (Todeschini & Baccini, 2016). The analysis of scholarly journals as social institutions of
science appears less developed. En efecto, scholarly journals connect members of academic commu-
niidades (Potts et al., 2017). The editorial boards of journals constitute the first layer of such a com-
munity. They act as gatekeepers of science: Ellos son, directly or indirectly, responsible for the
refereeing processes and they decide which papers are worth publishing in their journals (Crane,
1967; Hoenig, 2015). The stronger the link between the prestige of journals and the career ad-
vancement of scholars, the stronger the academic power exercised by the members of an editorial
board. From this point of view, it is possible to consider editorial boards as engines of academic
fuerza. A possible way to study the role of editors consists in observing the presence of the same
editors on the boards of different journals. The network of journals generated by the presence of the
same person on the editorial board of more than one journal is called an interlocking editorship (IE)
network (Baccini, 2009; Baccini & Barabesi, 2011, 2014; Baccini et al., 2009). De este modo, if two journals
share the same persons on their editorial boards, it can be assumed that they have at least similar or
complementary editorial policies, because they are managed by similar groups of scholars (Baccini
et al., 2009). From another perspective, editors have the power to push the paper selection pro-
cesses toward decisions favoring departmental colleagues, or disciples, etcétera (Klein & DiCola,
2004; Laband & Piette, 1994). In this sense the IE network can be used to try to identify favoritism in
the refereeing process (Erfanmanesh & Morovati, 2017) or to illustrate the self-referentiality of
national communities of scholars (Baccini, 2009).

A second social community gathered around scholarly journals is constituted by the authors
of the published articles. Although many studies exist about authorship and co-authorship, solo
a few are focused on the communities of authors of specific journals (Potts et al., 2017). Sucesivamente, él
is possible to work analogously to the IE network by considering the journal network generated
by scholars authoring papers in different journals. The network among journals generated by the
presence of the same authors in different journals could be called the interlocking authorship
(IA) network. A lo mejor de nuestro conocimiento, this kind of network has been rarely explored
(Brogaard et al., 2014; En, Sugimoto, & Cronin, 2013; En, Sugimoto, & Jiang, 2013). In the IA
network, the proximity between two journals can be considered proportional to the number of
common authors. Such a proximity is, in some sense, intellectual, because it is based on the
choices made by authors on where to publish their papers, and on the decisions of editors to
accept or reject those papers. The community of authors around a journal thus reflects to a cer-
tain degree the contents of the journal and the activity of the gatekeepers of the journal. If two
journals are in proximity, it can be supposed that they have similar contents and that their
editorial policies are similar or complementary.

Scholarly journals contribute to the definition of the intellectual landscapes of research fields.
Co-citation analysis is probably the best known instrument for studying the intellectual proximity
among authors, documentos, and journals (Pequeño, 1973). Por ejemplo, if two authors are frequently
cited together in many different papers, this suggest that these two persons are somehow intel-
lectually connected by the topic or methodology of their work. Similarmente, two different journals
often cited together in the same paper suggest that these journals are connected. The more often
they are cited together, the stronger the link between these authors or journals. We thus obtain a
network connecting the journals based on their being cited together often. Let us call this net-
work CC, as it is based on a different measure than those obtained through IE and IA.

In this paper we consider the IE, IA, and CC networks of journals described above and
compare the degree of proximity of journals in the three networks. The first intuitive question
is to what extent these three networks are similar. If two journals are well connected in the CC
network—that is, if they have strong intellectual proximity—does a similar proximity exist in

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Intellectual and social similarity among scholarly journals

the IA or IE networks? The basic idea is to explore to what extent the social proximity among
journals observed in the network of the editorial boards is similar to the social/intellectual
proximity observed in the IA network and in the intellectual proximity in the CC network.

This question is explored by considering the IE, IA, and CC networks in three fields: eco-
nomics (EC), Estadísticas (STAT), and information and library science (ILS). Two reasons justify the
choice of the three fields. The first is practical: For the three fields, data on the editorial boards
of journals were already available because they had been collected by two of the authors in a
previous research project. Data on editorial boards has to be collected by hand. Por eso, su
availability is a big advantage. The second reason is that scholars in the three fields differ in the
way they use scholarly journals as outlets for publishing research results. Although in statistics,
journal articles are largely dominant, scholars in economics and ILS continue to write book
chapters and books (Kulczycki et al., 2018). Hence the similarity analysis considered three
different scholarly communication contexts. For each field we compare the three networks
as a whole by using suitable statistical techniques. Después, for each field, we partition
the three networks in ”communities of journals” and we analyze the coherence of these com-
munities between pairs of networks.

The paper is organized as follows. Sección 2 describes the network data used in the paper.
Sección 3 studies the dissimilarities and the generalized distance correlations between networks.
Sección 4 contains the analysis of the correlation between detected communities. Sección 5 dis-
cusses the main results and concludes.

JOURNAL NETWORKS DATA

The journal networks considered here are all one-mode (Wasserman & Faust, 1994). In an IE
network, nodes are scholarly journals and the edge between two journals indicates that at least
one scholar sits on the board of both. Each edge can be weighted by the number of common
editors between the linked journals. Analogously, in the IA networks, the edges between jour-
nals are generated by common authors and the weight of the edge is the number of common
autores. Finalmente, in a CC network, the edge between journals is generated by the fact that the
two journals are cited together in at least one article; the weight of the edge is the number of
articles citing the two journals together.

We have constructed the three networks (IE, IA, CC) for the three fields, for a total of nine
redes. For IE networks, as anticipated, we used three existing databases, each containing
the journal editorial boards in a given year. Details on their collection and normalization can
be found in the papers referenced below. Además, IA and CC networks were constructed by
using Web of Science (WoS) data for a 5-year period, starting from the year for which the IE
was recorded. The raw data for the nine one-mode networks can be downloaded from https://
doi.org/10.5281/zenodo.3350797.

For economics, we considered a set of 169 journals listed in the EconLit database and
indexed in the Journal Citation Reports for the year 2006. The IE network (Cifra 1) was ex-
tracted from the database collected by Baccini and Barabesi (2010) for the year 2006. The IA
(Cifra 2) and CC (Cifra 3) networks for economic journals were built on WoS data by con-
sidering respectively the authors of and the references in the papers published in the journals
in the years 2006–2010.

For the field of statistics, the set includes the 79 journals listed in the category ”Statistics and
probability” of the Journal Citation Reports for the year 2005. IE data (Cifra 4) are the ones
collected in Baccini et al. (2009) for the year 2006. Similarmente, for the discipline of statistics, IA

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Intellectual and social similarity among scholarly journals

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Cifra 1.

Interlocking editorship network of economic journals.

(Cifra 5) and CC (Cifra 6) networks were built using WoS data by considering papers pub-
lished in the years 2006–2010.

Finalmente, for the domain of ILS, the set includes the 59 journals listed in the category
”Information science and library science” of the Journal Citation Reports for the year 2008.

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Cifra 2.

Interlocking authorship network of economic journals.

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Intellectual and social similarity among scholarly journals

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Cifra 3. Co-citation network of economic journals.

IE data (Cifra 7) are the ones collected in Baccini and Barabesi (2011) for the year 2010.
De nuevo, IA (Cifra 8) and CC (Cifra 9) networks were built on WoS data, by considering papers
published in the years 2010–2014.

In Figures 1–9 the size of a node is proportional to its degree and the width of an edge is
proportional to the value of the link. In the IE network, Por ejemplo, the size of a node is pro-
portional to the number of journals to which it is linked; the width of the link between two
nodes is proportional to the number of their common editors. For each field, the visual

Cifra 4.

Interlocking editorship network of statistical journals.

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Intellectual and social similarity among scholarly journals

Cifra 5.

Interlocking authorship network of statistical journals.

comparison of the three networks is hardly informative. Por ejemplo, it is apparent that for all
three fields, the IE networks are less connected than the IA and CC networks. También, in the cen-
ter of the networks there are not always the same journals, and a journal may have a different
size in the three networks. We therefore need a better way of comparing networks.

3. DISSIMILARITIES AMONG NETWORKS

For each network, it is possible to build a pseudo-measure of the distance among journals by
calculating a matrix of dissimilarities. The Jaccard index was adopted as a dissimilarity

Cifra 6. Co-citation network of statistical journals.

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Intellectual and social similarity among scholarly journals

Cifra 7.

Interlocking editorship network of information and library sciences journals.

measure (for more details on the Jaccard index, see e.g. Levandowsky and Winter, 1971).
Más precisamente, if A and B represent the sets containing the members of the editorial boards
of two journals, the Jaccard dissimilarity is defined as

J A; Bð

j − A∩B
Þ ¼ A∪ B
j
j
A∪ B
j
j

(1)

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Cifra 8.

Interlocking authorship network of information and library sciences journals.

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Intellectual and social similarity among scholarly journals

Cifra 9. Co-citation network of information and library sciences journals.

Como ejemplo, in the IE network, the similarity among journals is proportional to the number of
common editors on their boards. Por eso, the minimum dissimilarity J(A, B) = 0 is reached when two
journals have exactly the same editorial board (es decir. all the editors of a journal are also the editors of the
other and vice versa). The maximum dissimilarity J(A, B) = 1 is reached when two journals have no
editors in common. In order to compare the three dissimilarity matrices arising from co-citation, ed-
itorial board, and author networks for each discipline, we adopt the generalized distance correlation
Rd suggested by Omelka and Hudecova (2013) on the basis of the seminal proposal by Székely et al.
(2007). It should be remarked that such a correlation index avoids the drawbacks emphasized by
Dutilleul et al. (2000) when the classical Mantel coefficient is assumed instead (Omelka &
Hudecova, 2013). Por eso, we considered the three possible couples of networks and we computed
for each discipline. It is worth noting that Rd is somehow similar to
the corresponding values of
the squared Pearson correlation coefficient—and hence
should be interpreted as a generaliza-
tion of the usual correlation coefficient. Más precisamente, Rd is defined in the interval [0, 1] in such a
way that values close to zero indicate no or very weak association, and larger values suggest a stron-
ger association, which is perfect for Rd = 1—and similar considerations obviously hold for
(para
more details, see e.g. Omelka & Hudecova, 2013). The generalized distance correlation was eval-
uated in the R computing environment (R Core Team, 2018) by using the function dcor in the
package energy (Rizzo & Székely, 2018). These values of

are reported in Table 1.

ﬃﬃﬃﬃﬃﬃ
Rd

pag

ﬃﬃﬃﬃﬃﬃ
Rd

pag

From the analysis of Table 1, the dependence between the considered dissimilarity matrices
ﬃﬃﬃﬃﬃﬃ
Rd
is apparent. En efecto, the observed values of
are greater than (or nearly equal to) el valor
0.5 for each combination of networks in the three disciplines. Además, the permutation test
for assessing independence, as proposed by Omelka and Hudecova (2013), was also carried
afuera. The statistical details of the permutation test are rather involved, even if they are clearly
explained by Omelka and Hudecova (2013). Loosely speaking, the rationale behind the test
stems from the fact that, under the null hypothesis of independence, the generalized distance
correlation should not be affected by a random permutation of the rows and the corresponding
columns of the “centred” distance matrices. The permutation principle is widely adopted in order to
carry out nonparametric inference, because assumptions are minimal and practical

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Intellectual and social similarity among scholarly journals

Mesa 1. Generalized distance correlations between networks

Networks
co-citation vs editor

pag

ﬃﬃﬃﬃﬃﬃ
Rd

Estadísticas
0.5947

Information and
library sciences
0.5386

Ciencias económicas
0.5228

P-value

0.00001

0.00058

0.00001

co-citation vs author

editor vs author

pag

ﬃﬃﬃﬃﬃﬃ
Rd

0.6431

P-value

0.00001

pag

ﬃﬃﬃﬃﬃﬃ
Rd

0.5985

P-value

0.00001

0.6389

0.00001

0.4969

0.00382

0.7518

0.00001

0.5112

0.00001

implementation is often straightforward (see e.g. Lehmann & Romano, 2005, Sección 10). The per-
mutation test of independence was in turn implemented by using the package energy (Rizzo &
Székely, 2018). The significance of the test statistic was computed by means of the R function
dcov.test (for more details see Omelka & Hudecova, 2013). On the basis of the achieved p-values
given in Table 1, the independence hypotheses can be rejected at the significance level α = 0.01.
Because the three statistical tests within each discipline are obviously dependent, we also consider
the Bonferroni procedure in order to control the familywise error rate (for more details, see e.g.,
Bretz et al., 2011). De este modo, by assuming such a procedure and a global significance level given by
un = 0.01, the marginal independence hypotheses may be rejected if the corresponding p values are
less than α/3 = 0.0033, which is the case for all the considered tests—except the editorial board and
author networks for ILS. Sin embargo, it is worth remarking that—even in this case—the corre-
sponding p-value is just slightly larger than the threshold. Por eso, the co-citation, editorial
board, and author networks display structures which may be considered associated for each
considered discipline—at least on the basis of the considered dissimilarity matrices.

4. CORRELATIONS AMONG COMMUNITIES OF JOURNALS

The proximity among journal networks can be explored by focusing on communities of jour-
nal. The first step consists in detecting communities inside each network; the second in ver-
ifying the degree of association between the communities detected in different networks of the
same field. A nonoverlapping community of nodes of a network is a set of nodes densely
connected internally and only sparsely connected with external nodes. Each network is parti-
tioned in communities by using the Louvain algorithm (Blondel et al., 2008) as implemented in
the software Pajek (de Nooy et al., 2018). It consists in the optimization of the modularity of
the network (Hombre nuevo, 2004; Hombre nuevo & girvan, 2004). The quality of the partition is quan-
titatively measured by modularity values. Mesa 2 reports the values of modularities and the
resolution parameters adopted for optimization. The resolution parameter is used to control
the size of the communities detected; higher values of the parameter produce larger number
of communities and vice versa. Mesa 2 also reports the number of communities detected.

For all the pairs of networks inside each research field, the association between the re-
sulting communities is then analyzed by using statistical techniques as available in Pajek
(de Nooy et al., 2018). All the indicators considered are adopted under an exploratory ap-
proach. χ 2 statistics provide an index aiming to assess the degree of independence of the

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Intellectual and social similarity among scholarly journals

Mesa 2. Main features of networks and communities

Density

Estadísticas
CC
0.671

IE
0.121

IA
0.91

Information and library sciences
IA
CC
0.764
0.644

IE
0.0935

Ciencias económicas
CC
0.566

IE
0.07

IA
0.744

Average degree

9.443

52.379

70.962

5.423

37.356

44.339

11.751

95.053

125.001

Isolated journals

Resolution

Modularity

norte. communities

norte. nonisolated communities

E-I unweighted

E-I weighted

0.8

0.4

0.108

0.171

0.528

0.266

0.329

0.444

0.09

0.218

−0,04

0.22

0.41

−0.309

−0.141

−0,02

−0.425

−0.651

0.201

−0.355

0.2

0.108

0.322

0.435

−0.328

−0.202

0.131

0.038

partitions of each pair of networks. Cramér’s V is a measure of association giving a value between
0 (no association) y +1 (perfect association) (Cramér, 1946). Rajski’s coherence (Legendre &
Legendre, 1998) is presented in three variants, all defined in the [0, 1] range: a symmetrical version
indicating the coherence between each pair of classification and two asymmetrical versions
called in Table 3 “Rajski’s right” and “Rajski’s left.” When the communities in the IE-CC
networks are considered, Rajski’s left indicates the extent to which the first communities
classification IE is able to predict the second communities classification CC; Rajski’s right
indicates instead the extent to which the second classification is able to predict the first.
Finalmente, the adjusted Rand index measures the degree of association between partitions
and is bounded between ±1 (Hubert & Arabie, 1985). All indices are reported in Table 3.

For the three fields analyzed here, we observe that the IE is the least dense network and the
network with the lowest average degree. For the three fields, the CC networks are in the in-
termediate position for density and average degree, and finally the IA networks have the high-
est values of density (0.91 for statistics) and average degree (Wasserman & Faust, 1994).

En general, the community detection algorithm was more successful in sparser networks:
For the three fields, the values of modularity are indeed the highest for the IE network, enterrar-
mediate for CC, and lowest for IA. In the IE networks many detected communities are actually

Mesa 3. Association indexes between communities

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IE-CC

Estadísticas
IE-IA

CC-IA

Information and library sciences
IE-IA
CC-IA
IE-CC
80.51 (26)

68.59 (18) 118.88 (27) 80.85 (6) 75.45 (26)

68.68 (4) 199.58 (45) 255.34 (60) 207.29 (12)

IE-CC

Ciencias económicas
IE-IA

CC-IA

χ2 (d.f.)

Cramér’s V

Rajski’s

Rajski’s right

Rajski’s left

0.659

0.207

0.448

0.278

Adj. Rand index

0.249

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0.708

0.291

0.527

0.394

0.346

0.715

0.799

0.312

0.279

0.524

0.624

0.435

0.336

0.444

0.296

0.826

0.301

0.679

0.351

0.303

0.763

0.434

0.617

0.594

0.503

0.627

0.171

0.398

0.232

0.147

0.615

0.207

0.431

0.284

0.2

0.639

0.229

0.351

0.398

0.265

286

Intellectual and social similarity among scholarly journals

isolated journals (es decir. journals with no common editors with other considered journals). En
every case, the number of communities detected in the IE networks is always bigger than
the number of communities detected in the other networks.

Information and library sciences is the field where the communities are more easily detectable
and more clearly defined, as shown by the highest modularity values and by the lowest values of
the E-I indices (Mesa 2) (de Nooy et al., 2018). In particular for the IA network, communities were
detected by adopting a resolution value of 0.8. This resolution was preferred to the value of 1
adopted for all the other networks, because the resulting communities exhibited better E-I indices.
With a value of resolution of 1 the IA network is partitioned into four communities, with modu-
larity 0.257, E-I unweighted = 0.255 and E-I weighted = −0.083. The E-I index was calculated as
the difference between the number of edges within communities and the number of edges be-
tween communities; that difference is then divided for the total number of edges of the network.
The weighted version of the index is calculated by considering the value of the edges. The range
of the index is between −1 (all edges are inside communities) y 1 (all edges are between com-
munities). The χ 2 values show that the detected communities for the three networks are not inde-
pendiente. The association between the partitions of communities as measured by Cramér’s V is
alto. The highest level of association as measured by the adjusted Rand’s index is found between
communities detected in the CC and IA networks. Rajski’s right indicates that the communities
detected in the IE network predict well the communities detected in the other networks.

The field of statistics is in an intermediate position: The values of modularity are very low
for CC and IA networks, but nevertheless the resulting partitions have negative values of the E-I
weighted indices. Communities in the IE network are more easily detectable and more clearly
defined than in the IA and especially in the CC network. Also in this field, the χ 2 values show
that the detected communities for the three networks are not independent. The association
between the partitions of communities is a bit higher between CC and IA than for the other
pairs of networks. Also in this case Rajski’s right indicates that the communities of the IE net-
work predict well the communities in the other two networks.

For the case of economics, community detection is particularly problematic: Small changes of
the value of the resolution parameter changed substantially the number of detected communities
and the values of the indicators considered. For CC and IA, the community detection procedure
results in very low values of modularity and in positive values of E-I. Only for the IE network is the
modularity around 0.5 and the value of the E-I weighted index less than zero. Also in this field the
χ 2 values show that detected communities for the three networks are not independent. The asso-
ciation between the partitions of communities is the lowest of the three fields analyzed in this
paper. Rajski’s right indicates also for economics that the communities in the IE network predict
the communities in the other two networks, but the values of the index are the lowest of the three.

5. DISCUSSION AND CONCLUSIONS

The main aim of this paper was to explore, by using suitable quantitative techniques, to what
extent the intellectual proximity among scholarly journals is also a proximity in terms of social
communities gathered around the journals.

To represent the intellectual proximity among journals we have used the CC network. Para
information about the academic communities around journals, we have considered the net-
works of journals generated by authors writing in more than one journal as well as the net-
works generated by scholars sitting on the editorial boards of more than one journal. The first
step in the exploratory analysis consisted of comparing the whole structure of the networks on
the basis of dissimilarity matrices. The CC, IE, and IA networks appear to be associated for all

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Intellectual and social similarity among scholarly journals

the three considered fields. The second step consisted of partitioning the IE, IA, and CC net-
works into communities and then verifying the degree of association among the detected com-
munities. The results of that analysis show that the communities detected in the three networks
are not independent for the three research fields considered. The results of both approaches
are coherent in showing that the strongest correlations between networks is between CC and
IA for the three fields. Lower and similar correlations were obtained for CC and IE, and for IE
and IA. When communities are considered, the strongest association between communities is
between CC and IA networks; the minimum level of association is between IE and CC.

A lo mejor de nuestro conocimiento, the only similar analysis was performed by Ni, Sugimoto, y
Cronin (2013) in their investigation of scholarly communication. They focused on ILS by con-
sidering networks of journals generated by common authors, CC, common topics, and common
editores. They descriptively compared clusters of journals between networks and calculated a
correlation between pairs of matrices by using the quadratic assignment procedure. Their results
appear to be coherent with those presented here, because they estimated statistically significant
correlations for networks of journals based on authors, co-citation, and editors.

En general, the results of our analysis show that intellectual proximity is also a proximity among
authors and, more surprisingly, among editors of the journals. This leads to the question of whether
the structures obtained could ever be independent if the same set of people were predominantly
involved in the editorial boards, the publishing of papers, and the citing of papers. In that case the
structures are just a consequence of the existence of a publishing and gatekeeping élite in the
considered research fields. This is a topic worth investigation by considering the dual networks
that we used for generating the nine one-mode networks analyzed in this paper. At the current
state of knowledge, it is only possible to affirm that the map of editorial power, the map of intel-
lectual proximity, and the map of author communities tell similar stories. The fact that the results
are comparable for the three fields studied suggests that the method presented here is more gen-
erally applicable to any scientific field and that there should be in general a coherence among
journals at the three scales of editorial boards, authors’ choice of publications, and co-citations.

EXPRESIONES DE GRATITUD

The authors are grateful to the two reviewers for their useful comments.

CONTRIBUCIONES DE AUTOR

Alberto Baccini: conceptualization, data curation, investigación, formal analysis, methodol-
ogia, writing. Lucio Barabesi: conceptualization, formal analysis, methodology, writing.
Mahdi Khelfaoui: data curation, investigación, formal analysis, visualization, writing. Yves
Gingras: conceptualization, methodology, writing.

CONFLICTO DE INTERESES

Los autores no tienen intereses en competencia.

INFORMACIÓN DE FINANCIACIÓN

Alberto Baccini is the recipient of a grant by the Institute For New Economic Thinking Grant ID
INO17-00015.

DISPONIBILIDAD DE DATOS

The raw data for the nine one-mode networks can be downloaded from https://doi.org/
10.5281/zenodo.3350797.

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