RESEARCH ARTICLE
Predatory publishing in Scopus:
Evidence on cross-country differences
Vít Macháček1,2
and Martin Srholec1
1CERGE-EI, a joint workplace of Charles University and the Economics Institute of the Czech Academy of Sciences,
Prague, Czech Republic
2Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic
Keywords: academic misconduct, Beall’s list, open access, predatory journal, research system,
research policy
ABSTRACT
Predatory publishing represents a major challenge to scholarly communication. This paper
maps the infiltration of journals suspected of predatory practices into the citation database
Scopus and examines cross-country differences in the propensity of scholars to publish in
such journals. Using the names of “potential, possible, or probable” predatory journals and
publishers on Beall’s lists, we derived the ISSNs of 3,293 journals from Ulrichsweb and
searched Scopus with them. A total of 324 of journals that appear in both Beall’s lists and
Scopus, with 164,000 articles published during 2015–2017 were identified. Analysis of data
for 172 countries in four fields of research indicates that there is a remarkable heterogeneity. In
the most affected countries, including Kazakhstan and Indonesia, around 17% of articles were
published in the suspected predatory journals, while some other countries have no articles
in this category whatsoever. Countries with large research sectors at the medium level of
economic development, especially in Asia and North Africa, tend to be most susceptible to
predatory publishing. Policy makers and stakeholders in these and other developing countries
need to pay more attention to the quality of research evaluation.
INTRODUCTION
1.
“Predatory” (or fraudulent) scholarly journals exploit a paid open-access publication model:
The publisher does not charge subscription fees but receives money directly from the author of
an article that becomes accessible for free to anyone. However, this entails a conflict of inter-
ests that has the potential to undermine the credibility of open-access scholarly publishing
(Beall, 2013). Authors are motivated to pay to have their work published for the sake of career
progression or research evaluation, for instance (Bagues, Sylos-Labini, & Zinovyeva, 2019;
Demir, 2018; Kurt, 2018). In return, predatory publishers turn a blind eye to any limitations
of papers during peer review in favor of generating income from authors’ fees; the worst of
them fake the peer-review process and print almost anything for money, without scruples
(Bohannon, 2013; Butler, 2013).
So far, only a handful of studies have examined the geographical distribution of authors
published in journals suspected of predatory practices by Beall (2016). On a sample of 47 such
journals, Shen and Björk (2015) found that the authors were highly skewed to Asia and Africa,
primarily India and Nigeria. Xia, Harmon et al. (2015) examined seven pharmaceutical
a n o p e n a c c e s s
j o u r n a l
Citation: Macháček, V., & Srholec, M.
(2022). Predatory publishing in Scopus:
Evidence on cross-country differences.
Quantitative Science Studies, 3(3),
859–887. https://doi.org/10.1162/qss_a
_00213
DOI:
https://doi.org/10.1162/qss_a_00213
Received: 11 February 2022
Accepted: 6 August 2022
Corresponding Author:
Vít Macháček
vit.machacek@cerge-ei.cz
Handling Editors:
Ludo Waltman and Vincent Larivière
Copyright: © 2022 Vít Macháček and
Martin Srholec. Published under a
Creative Commons Attribution 4.0
International (CC BY 4.0) license.
The MIT Press
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Predatory publishing in Scopus
journals and also identified the vast majority of authors as being from Southeast Asia, predom-
inantly India, and, to a lesser extent, Africa. Demir (2018) combed through 832 suspected
predatory journals and confirmed that by far the greatest number of authors are from India,
followed by Nigeria, Turkey, the United States, China, and Saudi Arabia. Wallace and Perri
(2018) focused on 27 such journals in economics, in which the authors were most frequently
from Iran, the United States, Nigeria, Malaysia, and Turkey.
No matter how insightful these studies are in revealing from where contributors to sus-
pected predatory journals originate, we still know very little about the magnitude of the prob-
lem for the respective countries and regions. India appears to be the main hotbed of predatory
publishing, but in the context of India’s gigantic research system, this may be much ado about
little. All the countries cited above are, unsurprisingly, quite large. Could it be that some
smaller countries are actually far worse off, though they do not stand out in the absolute
figures? Just how large is the propensity to predatory publishing at the national level? Which
countries are most and least affected by this problem, and why?
Existing literature provides very scant evidence along these lines and the studies at hand are
limited to individual countries and use different methodologies, so the results are not easily
comparable. For example, Perlin, Imasato, and Borenstein (2018) found that suspected predatory
journal articles accounted only for about 1.5% of publications in Brazil, while Bagues et al.
(2019) showed that around 5% of researchers published in such journals in Italy. No study has
yet examined the penetration of national research systems by predatory publishing in a broad
comparative perspective. Systematic scrutiny of cross-country differences worldwide is lacking.
This paper helps to fill that gap by examining the propensity to publish in suspected pred-
atory journals for 172 countries in four fields of research during the 2015–2017 period. Using
the names of journals and publishers on lists by Beall (2016), we derived the ISSNs of 3,293
titles from Ulrichsweb (2016) and searched Scopus (2018a) for them. We identified 324
matched journals with 164,000 indexed articles. Next, we downloaded from Scopus the
number of articles by author’s country of origin published in these journals and compared
the figures to the total number of indexed articles by country and field. The resulting database
provides more representative and comprehensive country-level evidence on publishing in the
suspected predatory journals than has been available in any previous study.
Our analysis indicates that there is remarkable heterogeneity in the propensity to publish in
suspected predatory journals across countries. In line with earlier evidence, the most affected
countries are in Asia and North Africa, but they are not necessarily the same ones cited above.
In the most affected countries, including Kazakhstan and Indonesia, around 17% of articles
were published in the suspected predatory journals, while some countries have no articles
in this category whatsoever. India’s situation also looks daunting, but it is not the worst off.
Econometric analysis of cross-country differences shows that countries with large research sec-
tors at the medium level of economic development tend to be most susceptible to predatory
publishing. Arab, oil-rich, and/or eastern countries are also particularly vulnerable. To the best
of our knowledge, this is the first systematic attempt to pin down national research systems at
the most risk of falling into the trap of predatory publishing.
No doubt, the lists of predatory, questionable, or fake journals are controversial. It should
be emphasized that the purpose of this paper is not to evaluate the suspected predatory
journals and assess whether they deserve this label or not. Beall (2015, 2016) developed
the identification criteria and put his reputation on the line by curating the lists, which in turn
became widely used in empirical research on this topic (see, for instance, Bagues et al. (2019),
Bohannon (2013), Bolshete (2018), Cobey, Grudniewicz et al. (2019), Demir (2018, 2020),
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Predatory publishing in Scopus
Downes (2020), Erfanmanesh and Pourhossein (2017), Frandsen (2017), Ibba, Pani et al.
(2017), Kurt (2018), Perlin et al. (2018), Shen and Björk (2015), Shamseer, Moher et al.
(2017), Wallace and Perri (2018), and Xia et al. (2015)). We use this source of data and our
competences in comparative research to throw new light on cross-country differences in the
propensity to publish in them. In our view, this helps to deepen understanding of the problem
of predatory publishing.
The paper proceeds as follows. Section 2 reviews the existing literature on predatory pub-
lishing, introduces Beall’s lists, and elaborates on their validity and limitations. Section 3
explains how the data set has been constructed and how it can be used. Section 4 provides
an exploratory analysis of differences across countries and relevant country groups and pre-
sents econometric tests of the relationships hypothesized. The Section 5 summarizes the key
findings and pulls the strands together.
2. TAKING STOCK OF THE LITERATURE
2.1. Predatory Publishing
Jeffrey Beall popularized the term predatory publishing on his blog (Beall, 2016). It is used to
describe the practice of abusing paid open-access scientific publishing. In contrast to standard
subscription-based models, authors publishing via paid open access do business directly with
publishing houses. They pay article processing fees directly to the publisher of the journal.
Both authors and publishers are motivated to publish articles. Predatory journals perform only
vague, pro forma, or (in some cases) no peer review, and allow publication of pseudo-
scientific results (Bohannon, 2013; Butler, 2013). Predatory journals have also been accused
of aggressive marketing practices, having fake members of editorial boards, and amateur
business management (Beall, 2015; Cobey, Lalu et al., 2018; Eriksson & Helgesson, 2017a).
However, the latter are only side effects. We use the term predatory journals to signify journals
suspected of abusing paid open access to extort fees from authors and following significantly
flawed editorial practices.
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The open-access model, although it is a defining element of predatory journals, is not at
fault per se. The inherent conflict of interest does not have to be exploited. There are effective
means to ensure the quality of the editorial practices of journals. Databases dedicated to sup-
porting open access, such as the Directory of Open Access Journals, are already working to
develop operational mechanisms to guarantee quality and to employ transparency measures
such as open peer review, which can easily detect fraudulent publishers. Journals not perform-
ing peer reviews have admittedly nothing to report here. The existence of predatory journals
does not mean that the movement calling for democratizing communication of scientific
results is fruitless.
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Nevertheless, it is challenging to recognize a predatory journal in practice, because there is
no clearly defined boundary between journals that follow ethical editorial standards and those
that are merely vehicles for exploiting publication fees. Most often, to facilitate awareness and
identification, lists are used to identify suspected predatory journals. The most prominent
example is Jeffrey Beall’s blog (Beall, 2016), which was shut down at the beginning of 2017
(Straumsheim, 2017)1. A private company, Cabells, subsequently began to offer a similar list
(Silver, 2017), the content of which, however, is locked behind a paywall (Cabells, 2022).
China has announced the formation of a list of “poor quality” journals (Cyranoski, 2018),
1 Anonymous authors continue with Beall’s work and regularly update his lists on a new website (Anonymous,
2022).
Quantitative Science Studies
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Predatory publishing in Scopus
which was followed by the creation of a list of questionable journals by the National Science
Library of the Chinese Academy of Sciences (Zhang, Wei et al., 2022), but this list seems to be
far narrower in scope than both of its predecessors.
The inclusion of individual journals on a list should be based on rigid and transparent cri-
teria. Beall (2015) provided a list of criteria that he used to make decisions about journals and
publishers. Eriksson and Helgesson (2017a) and Cobey et al. (2018) have also suggested a
similar list of characteristics to identify predatory journals. The key set of Beall’s criteria points
directly to the most salient problem of dubious editorial practices: (“Evidence exists showing
that the publisher does not really conduct a bona fide peer-review”; “No academic informa-
tion is provided regarding the editor, editorial staff, and/or review board members”). However,
there is also a group of indicators concerning professionalism and/or compliance with ethical
standards (“The publisher has poorly maintained websites, including dead links, prominent
misspellings and grammatical errors on the website”; “Use boastful language claiming to be
a ‘leading publisher’ even though the publisher may only be a start-up or a novice organiza-
tion”, etc.).
Grudniewicz, Moher, and Cobey (2019, p. 211) addressed what they perceived as a lack of
agreed definition of predatory publishing by convening dozens of experts on this topic, who
arrived at the following consensus: “Predatory journals and publishers are entities that prior-
itize self-interest at the expense of scholarship and are characterized by false or misleading
information, deviation from best editorial and publication practices, a lack of transparency,
and/or the use of aggressive and indiscriminate solicitation practices,” which is arguably well
in tune with Beall (2015). But when it comes to criteria for the identification of predatory
journals in practice, they argue for relying on easy to detect defects, such as misinformation,
spamming, and/or spelling errors, rather than attempting to assess the quality of peer review.
By giving up on the latter, however, the identification is likely to miss predatory journals that
have become professionalized and manage to avoid the most obvious blunders, while still
neglecting peer review to maximize profits. In this regard, we concur with Moed, Lopez-
Illescas et al. (2022) that accepting manuscripts without any rigorous form of peer review is
the core characteristic of predatory journals that we thus should not leave out.
Kurt (2018) identified four pretexts that are often used to justify publication in predatory
journals by the authors: social identity threat; lack of awareness; high pressure to publish;
and lack of research proficiency. The common denominator is urgency. Researchers tend to
publish in these journals as a last resort and often refer to institutional pressure, a lack of expe-
rience, and fear of discrimination from “traditional” journals. Justifications for publishing in
predatory journals therefore appears to be a complex mix of factors operating at both personal
and institutional levels.
Demir (2018) and Bagues et al. (2019) also argue that the tendency to publish in predatory
journals is likely to be related to the quality of research evaluation in the country. The more the
research evaluation system relies on outdated routines such as counting articles indexed in
Scopus, Web of Science, or Medline, the higher incentive for researchers to publish in fraud-
ulent journals just to clinch points for outputs regardless of merit. In countries where the
culture of evaluation and peer pressure push researchers to publish in respectable journals,
there is little to no motivation to resort to predatory journals, as such behavior will harm
the researcher’s reputation.
Predatory publishing can be seen as wasteful of resources. Shen and Björk (2015) estimated
the size of the predatory market as high as US$74 million in 2014, based on article processing
fees, and the figure may well have grown significantly since. Perhaps more important than the
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Predatory publishing in Scopus
direct costs, however, are indirect costs stemming from the fact that the opportunity to bypass
the standard peer-review process leads researchers astray. Instead of spending their time pro-
ducing relevant insights, researchers may be increasingly prone to writing bogus papers that
only pretend to be scientific. If this occurs on an increasing scale, research systems are in peril.
The fact that research published in scientific journals is predominantly funded from public
sources only amplifies these concerns.
2.2. Beall’s Lists
Beall (2016) maintained two regularly updated lists of “potential, possible, or probable” preda-
tory journals and publishers, henceforth for the sake of brevity referred to as “suspected preda-
tory”: a) a “list of standalone journals,” which contains suspected individual journals; and b) a
“list of publishers,” which contains suspected publishers, most of which print multiple journals.
Crawford (2014b) went through every single item on Beall’s lists in late March and early
April 2014. He found 9,219 journals, of which 320 were from the list of standalone journals
and 8,899 from the list of publishers. Between 2012 and 2014, about 40% of those journals
published no or fewer than four articles; in other words, they were empty shells, and a further
20% published only a handful of articles. Another 4% consisted of dying or dormant journals
whose publications fell to a few articles in 2014, and 6% were unreachable (the web link was
broken, for instance). Overall, fewer than 30% of the identified journals published articles
regularly. Fewer than 5% of the journals appeared “apparently good as they stand,” meaning
that there was no immediate reason to doubt their credibility, which, however, did not imply
that they were in fact credible.
Shamseer et al. (2017) confirmed that Beall’s listed journals contained more spelling errors,
promoted bogus bibliometric metrics on their websites, and their editorial board members
were much more difficult to verify than those of “ordinary” journals. Bohannon (2013)
exposed flawed editorial practices by submitting fake scientific articles to journals of pub-
lishers from Beall’s list. The fake articles were accepted for publication by four-fifths of the
journals that completed the review process, which vindicates doubts about their peer review
routines. Bagues et al. (2019) showed that journals on Beall’s list tend to have low academic
impact and cite researchers admitting that the editorial practices of these journals are flawed.
Journals from these lists truly seem to be doubtful.
Moed et al. (2022) examined journals from Beall’s list of publishers with the help of bib-
liometric analysis using an updated version of a database published with an earlier version of
this paper. First, they found that the article output of a random sample of these journals that
were not indexed in Scopus had a strong tendency to dwindle, and two-fifths of them were
discontinued, confirming that most of them do not succeed in becoming regular publication
venues. Second, they found that the subset of these journals that were indexed in Scopus suf-
fered as a group a strong decline in citation impact and achieved impact levels far below that
of a control group of other open access journals indexed in Scopus, which they interpret as a
signal that in general their scientific relevance is inferior. Finally, however, they also pointed to
variability of the suspected predatory journals and that judging by bibliometric records the
inclusion of some publishers on Beall’s list might be questionable.
Strinzel, Severin et al. (2019) compared lists of predatory journals originated by Beall
(updated by its anonymous continuator) and more recently launched by Cabells Scholarly
Analytics (hereafter Cabells) as well as lists of credible journals compiled by the Directory
of Open Access Journals (DOAJ) and Cabells using data from December 2018. In terms of
journals and publishers indexed, they concluded that there was a considerable overlap
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Predatory publishing in Scopus
between the lists or predatory journals and even speculated that Cabells’ list may have used
Beall’s list as a source, but that there was essentially no or a very limited overlap between them
the lists of credible journals2. In terms of inclusion criteria, the analysis revealed that both of
the lists of predatory journals most frequently considered business practices, including the
business model, misinformation on location, spamming, and boastful language, but that these
aspects were far more dominant for Beall than Cabells, and that the main difference was that
Cabells used noticeably more criteria than Beall related to peer review and policy. However,
as also acknowledged as a limitation by the authors, the comparison relied only on the num-
ber of criteria in each category, not reflecting on their relative weight for indexing, which
could have differed significantly.
2.3. Limitations
As Eriksson and Helgesson (2017b) state, “the term ‘predatory journal’ hides a wide range of
scholarly publishing misconduct.” Some are truly fraudulent, while many others may operate
on the margins. However, Beall’s lists force us to work with a binary classification in which a
journal or publisher is considered either predatory or not. As Beall did not systematically
explain his decisions, it is not possible to make a more detailed quantification of “predatori-
ness,” though elaborated criteria exist.
Beall’s lists have been strongly criticized for the low transparency of his decision-making
process (Berger & Cirasella, 2015; Bloudoff-Indelicato, 2015; Crawford, 2014a). Although the
criteria are public, justification of decisions on individual journals and publishers is often not
clear and difficult to verify. Beall debated the decisions on his blog or on Twitter in some
important instances, but very often a journal or publisher was added to the list without justi-
fication being provided. The lack of comprehensive, rigid, and formal justification of Beall’s
judgments is a major drawback of his list.
In particular, caution is warranted when working with Beall’s list of publishers. Classifying
an entire publishing house as suspected predatory is a strong judgment, and it cannot be ruled
out that some journals which actually apply reputable standards have been listed along the
way. The list includes some publishers that maintain broad portfolios of dozens and even hun-
dreds of journals, some of which may not deserve the predatory label, so that using Beall’s list
may result in overestimations of true “predators.” It is likely that the overwhelming majority of
2 Strinzel et al. (2019) remain silent on what explains the differences between the lists of predatory journals
that they actually identified, but four factors driven by data issues are likely to be in play. First, Beall’s lists
include only open access journals and publishers, while Cabells’ list also includes subscription-based ones.
Second, one needs to keep in mind the findings by Crawford (2014b) that most of the journals on Beall’s lists
are empty shells, the findings by Moed et al. (2022) that they tend to dwindle or become discontinued, and
the findings by Siler, Vincent-Lamarre et al. (2021) that they become rebranded and morphed into different
outlets. The origins of Beall’s lists go back to the early 2010s and he was well known to focus on adding new
records rather than deleting possibly irrelevant old ones, while Cabells’ list was launched in 2017; therefore
many records that remain indexed in the former might not appear in the latter simply because they ceased to
exist or changed in the meantime. Third, Beall’s blog was shut down at the beginning of 2017 (Straumsheim,
2017) and even though its continuator pledges to update it regularly (Anonymous, 2022), it cannot be taken
for granted that the updates are as thorough as could have been in Beall’s original endeavor, and therefore
that new predatory journals that emerged in the meantime may have been recorded in Cabells’ list but not in
the updated Beall’s list. Finally, Strinzel et al. (2019) did not identify individual journals from Beall’s list of
publishers, while Cabells’ lists of journals and publishers are linked together; hence they compared the
restricted list of Beall’s standalone only journals with the all-encompassing list of Cabells journals, as a result
of which the overlap at the journal level had been underestimated.
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these journals are of poor quality, but poor quality is not a crime per se. One must, therefore,
keep in mind that the list of publishers has been painted with a relatively broad brush.
Nevertheless, respectable publishing houses should have zero tolerance for predatory prac-
tices. Just as in the banking sector, academic publishing services are based on trust, and if that
is lost, the business is doomed. A single journal with predatory inclinations that are not quickly
corrected by the publisher can substantially damage the entire brand. Beall’s suspected pred-
atory mark signals serious doubts about the publisher’s internal quality assurance mechanisms
at the very least.
The greatest controversy was triggered by inclusion of the Frontiers Research Foundation on
Beall’s list of publishers in October 2015. Beall defended this decision by pointing out several
articles that, according to him, should not have been published. According to critics of this
move, the Frontiers publisher is “legitimate and reputable and does offer proper peer-review”
(Bloudoff-Indelicato, 2015). Frontiers journals appear to be quite different from typical
suspected predatory outlets on the face value of their citation rates. Only four journals in
Frontiers’ portfolio of 29 included in this study are not ranked in the first quartile in at least
one field according to the Scimago SJR citation index (Scopus, 2018b). Most Frontiers journals
are also indexed in the Web of Science and the Directory of Open Access Journals. Hence,
judging by the relevance of Frontiers journals for the scientific community, there is a question
mark about their inclusion on Beall’s list.
Another concern arises from the time scale. The suspected predatory status used in this
study is derived from the content of Beall’s lists on April 1, 2016. Jeffrey Beall continually
updated his lists. However, the lists always reflect only current status, with no indication of
when the journal and publisher may have become suspected to be predatory. When looking
back in time, we may run into the problem of including in the predatory category records that
do not deserve that label, because the journal became suspected only a short time before its
inclusion on the list. In some cases, older articles published in journals that are currently sus-
pected to be predatory may have gone through a standard peer review. Hence, historical data
must be used with great caution.
Further, Beall’s lists are very likely to suffer from English bias. The lists contain mainly jour-
nals that at least have English-language websites. In regions in which a large part of scientific
output is written in other languages—such as in Latin America, Francophone areas, and coun-
tries of the former Soviet Union—estimates of the extent of predatory publishing based on
Beall’s lists may be underestimated, because Beall did not identify suspected predatory
journals in local languages. Likewise, Scopus covers scientific literature in English far more
comprehensively than publications in other major world languages. This bias should be kept
in mind when interpreting cross-country differences.
3. DATABASE
Our database was built in three steps. First, we compiled a comprehensive overview of
journals suspected of predatory practices by matching the lists of standalone journals and
publishers by Beall (2016) with records in the Ulrichsweb (2016) database, which provides
comprehensive lists of periodicals. Second, we searched the International Standard Serial
Numbers (ISSNs) of the journals obtained from Ulrichsweb in Scopus and downloaded data
on authors publishing in these journals by their country of origin. Third, we downloaded the
total number of indexed articles by country from Scopus. Ultimately, we obtained not only a
full list of suspected predatory journals listed in Scopus but, even more importantly, we also
obtained harmonized data on the propensity to publish in these journals by country, which
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allows us to shed new light on cross-country patterns (for a brief overview of the data gener-
ation process see Table 1).
Beall’s lists were downloaded on April 1, 2016. First, we identified all search terms in each
item on the lists. For some entries, Beall presented multiple versions of a journal designation; for
example, the journal name and its abbreviation. All available versions were used as a search term.
Next, we searched the terms in the Ulrichsweb database for the same day, using an automatic
Table 1. Overview of the data generation process
1. Obtaining the ISSNs of suspected predatory journals:
(a) Beall’s lists downloaded on April 1, 2016.
(b) The names on Beall’s lists were searched for using an automatic script in Ulrichsweb on
the same day.
(c) The entries found in Ulrichsweb were manually verified with the help of hypertext
links in Beall’s lists.
(d) 4,665 ISSNs of 3,295 individual journals were confirmed to be associated with
Beall’s lists.
2. Searching for “predatory” ISSNs in Scopus:
(a) The “predatory” ISSNs were searched for using an automatic script in Scopus on March
19, 2018.
(b) 439 ISSNs of 324 individual journals that had at least one entry in Scopus during the
period 2015–2017 were identified.
(c) The script downloaded the total number of indexed articles in each journal and the
number of these articles by the author’s
country of origin during the period 2015–2017.
(d) To avoid double counting articles in journals with ISSNs for both print and electronic
versions, duplicates were eliminated.
3. Downloading total number of articles in Scopus by country and field of research:
(a) The total number of indexed articles by country during the period 2015–2017 was
downloaded using the Scopus API on
March 19, 2018.
(b) The total number of indexed articles by country and field of research during the period
2015–2017 was downloaded using the Scopus API on March 5, 2020.
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script programmed in Python. When we searched for a standalone journal, the script used the
“title” field, and for the publisher, the script used the “publisher” field. In the end, the algorithm
saved all search results. The search request in Ulrichsweb was as follows for standalone journals:
+(+title:(“Academic Exchange Quarterly”))
and for publishers:
+(+publisher:(“Abhinav”))
The raw search on Ulrichsweb produced a database of 19,141 results linked to individual
entries on Beall’s list. Results without ISSNs were removed, as they were most probably not
listed in Scopus anyway; this reduced the database to 16,037 search results with 7,568 unique
ISSNs. The reduction is due to using multiple search terms related to the same entry and to the
“fuzziness” of the Ulrichsweb search3. To make sure that the journals are listed by Beall, the
remaining search results were checked manually. Beall’s lists consist of hypertext links, so we
compared the ISSN on the journal’s website with the ISSN on Ulrichsweb. If the two ISSNs
matched, the entry was retained; if they differed, the entry was removed from our database.
A publisher’s identity was confirmed if at least one ISSN listed on its website was found in an
entry linked to the publisher’s name on Ulrichsweb.
We confirmed 4,665 unique ISSNs associated with Beall’s lists. Many journals have dual
ISSNs, one for its print version and one for its electronic version. The number of individual
journals is 3,293, of which 309 featured on the list of standalone journals, 2,952 referred to
the list of publishers, and an additional 32 journals appeared on both lists, perhaps because
Beall did not recognize that the respective journal was from a publisher already on his list. For
simplicity, these journals are considered to belong to the list of publishers.
This is in line with the analysis of Crawford (2014b), which identified fewer than 3,000
journals that published articles regularly, and thus in fact appeared to be continuously in oper-
ation. Shen and Björk (2015) found around 8,000 journals that were “active” in the sense that
they published at least one article. However, many of these, as per Crawford (2014b), may not
publish significantly more than that and are not likely to be registered in databases. Note that
there are 1,003 hypertext links on the list of standalone journals, from which it follows that
more than two-thirds of these are not included in Ulrichsweb, let alone in more selective data-
bases. Apart from the unverified information on their web pages, there is no information about
them. Previous attempts to collect data on suspected predatory journals were far less
comprehensive4.
In the next step, we searched for the presence of these “predatory” ISSNs in the Scopus
(2018a) citation database during the period 2015–2017. Once again, this search was
performed using an automatic script programmed in Python. The search was performed on
March 19, 2018. For each ISSN detected in Scopus, the script downloaded not only the
total number of documents in the “article” category but also more detailed data on the
3 The Ulrichsweb search engine uses a “fuzzy” search that does not require perfect matching of strings. For
example, when we searched for Academe Research Journals, journals of Academic Research Journals were
also found. This is beneficial because the search is robust to typos, interpunction signs, and small errors
written in the search terms. However, it also requires careful manual verification of search results.
4 For example, Perlin et al. (2018) found only 1,100 ISSNs from both the list of publishers and the list of
standalone journals using an automatic website crawler and Demir (2018) analyzed only the list of standa-
lone journals.
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number of these articles by the author’s country of origin. The search request in Scopus was as
follows:
ISSN(1234-5678) AND DOCTYPE(ar) AND PUBYEAR > 2014 AND PUBYEAR < 2018
We identified 439 ISSNs of 324 individual journals with at least one entry in Scopus, of
which 37 appear on the list of standalone journals and 287 on the list of publishers. Thus,
nearly 10% of the journals in our database were indexed in Scopus. We detected 164,073
articles published in these journals, of which 22,235 occur in standalone journals and
141,838 come from the list of publishers, jointly making up 2.8% of all articles indexed
in Scopus during the period under consideration. Hence, the list of publishers, which
was rather neglected in previous empirical studies of predatory publishing, is the dominant
source. The journals were assigned to four broad fields of research: Health Sciences;
Life Sciences; Physical Sciences; and Social Sciences, based on the Scopus Source List
(Scopus, 2018b). If a journal is assigned to multiple fields, it is fully counted in each of them.
The database is available for download in Zenodo (Macháček & Srholec, 2022b).
Finally, we obtained data on the total number of articles in Scopus by author’s country of origin
and field of research during the period 2015–2017, which is the denominator required to compute
the penetration of suspected predatory journals in the article output of each country. The down-
load was performed on March 5, 2020. The search was performed using the following request:
AFFILCOUNTRY(country) AND SUBJAREA(field) AND DOCTYPE(ar) AND PUBYEAR >
2014 AND PUBYEAR < 2018
In the Scopus database, an article is fully attributed to a country if the affiliation of at least
one of its authors is located in that country. Joint articles by authors from different countries are
counted repeatedly in each participating country. Hence, the data measure article counts, not
fractional assignments. If articles in suspected predatory journals have fewer coauthors than
other articles, the predatory articles penetration is underestimated and vice versa; this can be
uneven across countries5. For some articles, Scopus reports the country of origin as
“undefined”; these are excluded from our analysis6.
How come there are suspected predatory journals in Scopus? Journals need to fulfil a
number of selection criteria to become indexed in the database (Scopus, 2019). However,
these criteria are either formal, such as having an ISSN, online availability, and English
language abstracts and titles, or derived from bibliometrics, such as a minimum threshold of
citations, article output and diversity of authors by country; or rely on policies in the sense of
what the journal declares what it does, for instance, in terms of peer review, rather than what it
does in practice. If these boxes are ticked, the journal is very likely to be accepted into Scopus.
Yet predatory journals that have managed to professionalize their business operation might
look like regular scientific outlets on the outside, their bibliometric profile might not differ that
much from other fringe journals, and they do not shy away from lying about their editorial
5 Unfortunately, the Scopus database does not directly provide harmonized data on the number of authors by
country that published in a journal. However, we can count the number of countries to which at least one
author of an article is affiliated by journal. Based on data for 324 suspected predatory journals and 23,387
other Scopus journals, the average numbers of country affiliations turns out to be 1.20 and 1.23, respec-
tively; hence there is not a significant difference and the bias is likely to be rather small.
6 Only 1,069 suspected predatory journal articles had an “undefined” country of origin. Hence, the over-
whelming majority of the articles found are included in our analysis.
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practices; deception is their defining feature. So this filter is not likely to be effective in keeping
out predatory journals that are good pretenders.
Scopus (2021) in a reaction to the earlier publication of this article (Macháček & Srholec,
2021) acknowledged that the database included the matched journals that have been identified
in our analysis, re-evaluated them and discontinued coverage of more than two-thirds of them:
All of the 137 suspicious titles mentioned in the paper have gone through the re-evaluation
process and as a result for 97 titles (71%), the decision was made to discontinue coverage in
Scopus. Also, all other journals listed by Beall that are mentioned in the paper have gone
through the re-evaluation process and as a result 65% of these titles were also discontinued.
(Scopus, 2021, p. 5)7
Moed et al. (2022) also reported that the indexing of about 60% of the suspected predatory
journals that they found in Scopus using the updated version of our database was discontinued
and that 2016 was a peak year in this respect. Scopus thus validated in hindsight that most of these
journals were problematic and probably should not have been included in the database in the first
place. In the meantime, however, papers published in these journals before they were discontinued
remain included in the database, possibly misleading unsuspecting readers by its content8.
At the same time, it needs to be emphasized that the fact that a matched journal has not been
discontinued by Scopus does not signify that it should be absolved from the suspected predatory
status. Scopus selection criteria and by extension the re-evaluation criteria, as discussed above,
rely heavily on bibliometric indicators and a journal’s declared policies and only partly check for
the attributes that have been proposed to identify predatory journals (Beall, 2015; Grudniewicz
et al., 2019; Strinzel et al., 2019), especially with regard to differences between what the journal
claims to be the case and reality. Scopus (2021) noted that formal complaints that have been
raised about publication standards are reflected in the re-evaluation process but does not pro-
vide any details (i.e., how many complains are collected, what they are typically about, how
they are fact-checked, and what represents an offense that is serious enough to discontinue cov-
erage). Until this mechanism of data collection becomes more transparent and widely used by
the research community, one cannot rule out that predatory journals fall through the cracks.
Admittedly, the suspected predatory journals that are indexed in Scopus represent only the
tip of the iceberg, which is not representative of the whole business. No matter how imperfect
the entry filter of Scopus turns out to be, the journals that made it through represent probably
the least ugly part. Nevertheless, from the research evaluation perspective, suspected preda-
tory journals indexed in respected citation databases are more dangerous than ordinary bogus
journals that few take seriously, because the indexation bestows a badge of quality9. All too
often, evaluations at various levels rely on this badge and blindly assume that whatever is
indexed counts and deserves to be supported by taxpayer’s money. Scopus-listed journals
are in practice considered “scientific” by many institutions and even national evaluation sys-
tems, such as in the Czech Republic (Good, Vermeulen et al., 2015), Italy (Bagues et al., 2019),
7 Macháček and Srholec (2021) identified 324 suspected predatory journals with at least one entry in Scopus
during the period 2015–2017, which does not correspond with the number of journals cited by Scopus
(2021), but the statement makes clear that most of those that were re-evaluated have been discontinued.
8 An early version of this paper came out in March 2017 (Macháček & Srholec, 2017) and was presented at
the Scopus Content Selection and Advisory Board Meeting in Prague on November 3, 2017.
9 We use Scopus rather than the Web of Science because it covers substantially more journals (Mongeon &
Paul-Hus, 2016) and is more vulnerable to suspected predators (Demir, 2020; Somoza-Fernández,
Rodríguez-Gairín, & Urbano, 2016).
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and probably many other countries as well. In particular, evaluation systems that do not check
the actual content using their own peer-review assessment are most exposed, but such assess-
ment tends to be expensive and difficult to organize, and thus is relatively rare in exactly the
environments that need this check most.
4. CROSS-COUNTRY PATTERNS
Out of more than 200 countries for which the data are available, we excluded dependent territories
and countries with fewer than 300,000 inhabitants. The analysis considers evidence from the period
between 2015 and 2017, because, as noted above, using older data risks that some of the journals
currently featured on Beall’s lists were not yet predatory at an earlier time. However, we use data
from 3 years to increase the robustness of the results. Only countries generating at least 30 articles
during this period are included in the analysis. As a result, the final sample consists of 172 countries,
which together account for the overwhelming majority of the world’s research activity.
The outcome variable used throughout the analysis is the share of articles linked to Beall’s
lists out of all articles by authors from the given country, hence the share of articles published
in suspected predatory journals out of total articles. First, we look at the global picture and
examine which countries are most and least affected by predatory publishing. Then, we
attempt to pin down the most salient patterns by considering differences between groups of
countries. Finally, we investigate how these patterns differ by broad fields of research.
Figure 1 displays the results on a world map. The darker the color, the higher the national pro-
pensity to publish in suspected predatory journals. The main pattern is visible at a quick glance:
The darkest areas are concentrated in Asia and North Africa. In contrast, Europe, North and South
America and Sub-Saharan Africa are relatively pale. Hence, generally speaking, both the most and
least developed countries tend to be relatively less affected, while developing countries with
emerging research systems, excepting those in South America, appear to be most in harm’s way.
Table 2 shows figures for the top and bottom 20 countries. Kazakhstan and Indonesia appear
to be the most badly affected, with roughly every sixth article falling into the suspected predatory
category. They are followed by Iraq, Albania, and Malaysia, with more than every tenth article
appearing in this category. Some of the most severely affected countries are also among the
Figure 1. Percentage of suspected predatory journal articles in total articles, 2015–2017. Source: Scopus (2018a), author’s calculations.
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Table 2.
countries, 2015–2017
Percentage of suspected predatory journal articles in total articles, top and bottom 20
Top 20
Bottom 20
Kazakhstan
Indonesia
Iraq
Albania
Malaysia
India
Oman
Yemen
Nigeria
Sudan
Jordan
Morocco
Syria
Philippines
Egypt
Palestine
Tajikistan
South Korea
Libya
Brunei
17.00
16.73
12.94
12.08
11.60
9.65
8.25
7.79
7.31
7.20
7.19
6.95
6.88
6.68
6.65
6.56
6.48
6.37
6.06
5.44
Guatemala
Solomon Islands
Bahamas
Angola
Honduras
Belarus
Congo, Dem. Rep.
Moldova
Afghanistan
Panama
Cambodia
Haiti
Guinea
Belize
Bhutan
Cape Verde
Chad
Maldives
North Korea
Turkmenistan
0.74
0.74
0.74
0.72
0.72
0.70
0.68
0.67
0.57
0.56
0.40
0.35
0.10
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Source: Scopus (2018a), author’s calculations.
largest in terms of population: India, Indonesia, Nigeria, the Philippines, and Egypt, which
underlines the gravity of the problem. However, small countries that might have been difficult
to spot on a world map, such as Albania, Oman, Jordan, Palestine, and Tajikistan are also seri-
ously affected. South Korea is by far the worst among advanced countries. All countries on the
top 20 list, excepting only Albania, are indeed in, or very near, Asia and North Africa.
Surprisingly, the opposite end of the spectrum, with the lowest penetration of suspected
predatory journal articles, is also dominated by developing countries, including some of even
the least developed. In several, for instance Bhutan, Chad, and North Korea, there are no
authors published in suspected predatory journals whatsoever. This is a rather diverse group
of countries scattered across continents. Nevertheless, they have one additional feature in
common: Most are small countries with underdeveloped research systems. In fact, 13 coun-
tries on the bottom 20 list produced fewer than 100 articles per year, on average. It may well
be that these research systems are small enough to make direct oversight of the actual content
of the manuscripts feasible, in which case predatory journal articles would have nowhere to
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hide. In large research systems with thousands of articles produced every year, predatory pub-
lishing may more easily fly under the radar of the relevant principals.
Table 3 summarizes the main patterns by presenting average propensities to publish in sus-
pected predatory journals by country groups, and provides details by the source list. First, we
Table 3.
Percentage of suspected predatory journal articles in total articles by country group and source list, 2015–2017
Number of countries
Total
Standalone
Publishers excl. Frontiers
Frontiers
Total excl. Frontiers
Source list
Country group
Geography
Europe
America
Asia
Africa
Oceania
Language
English spoken
French spoken
Spanish spoken
Arabic spoken
Other language spoken
Natural resources rents
Oil and natural gas
Other natural resources
40
28
49
50
5
37
21
21
21
86
24
39
Other countries
108
Income per capita
High income
Upper middle income
Lower middle income
Low income
Size of the research sector
Large size
Medium large size
Medium small size
Small size
All countries
48
44
48
30
43
43
43
43
172
Source: Scopus (2018a), author’s calculations.
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1.96
1.22
4.22
2.33
1.14
2.64
2.41
1.24
5.13
2.42
3.90
1.77
2.51
2.10
2.92
3.28
1.63
2.56
3.49
2.62
1.59
2.56
0.32
0.10
0.86
0.41
0.04
0.41
0.35
0.11
1.17
0.45
0.80
0.23
0.45
0.22
0.55
0.78
0.16
0.35
0.75
0.47
0.25
0.46
0.95
0.53
3.01
1.27
0.43
1.65
1.22
0.43
3.52
1.49
2.68
0.87
1.50
1.11
1.95
2.08
0.76
1.48
2.25
1.69
0.77
1.55
0.68
0.59
0.35
0.64
0.67
0.58
0.84
0.71
0.44
0.48
0.41
0.67
0.56
0.76
0.41
0.42
0.71
0.73
0.49
0.46
0.58
0.56
1.27
0.63
3.87
1.68
0.47
2.06
1.57
0.53
4.69
1.94
3.49
1.10
1.95
1.33
2.51
2.86
0.92
1.83
3.00
2.16
1.01
2.00
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reiterate the geographical dimension by continents, which confirms that the center of predatory
publication is in Asia, while the problem is relatively limited in North and South America. In fact,
Suriname, the most affected country in the latter, only ranks 50th in a worldwide comparison.
On average, Europe and Africa fall in between the two extremes, but this masks relatively large
national differences within these continents along the east–west and north–south axes, respec-
tively. Oceania is also little involved, but there are few countries in the region10.
Next, we examine differences by major language zones using indicators obtained from the
GeoDist database which measure whether the language (mother tongue, lingua franca, or a
second language) is spoken by at least 20% of the population of the country (Mayer &
Zignago, 2011). Only English, French, Spanish, and Arabic are recognized separately, as other
languages are not spoken in a sufficient number of countries. Note that, in contrast to geog-
raphy, assignment to language zones is not mutually exclusive, as more than one language can
be frequently spoken in the same country11.
Admittedly, language zones partly overlap with geography. This is most apparent in South
America, which is dominated by Spanish-speaking countries and thus, not surprisingly, the
propensities are very similar in both country groups. More revealing is perhaps the fact that
Arabic-speaking countries, which are concentrated in North Africa and the Middle East, are
the primary hotbeds of predatory publishing. English- and French-speaking countries are far
more geographically scattered across the globe.
As noted above, Beall’s lists may suffer from English bias. Nevertheless, our results only
partially support this expectation. English-speaking countries do not display significantly
higher propensities towards suspected predatory publishing than Francophone areas or coun-
tries speaking other languages. Spanish-speaking countries turn out to be different, perhaps
because we miss predatory journals published in Spanish by relying on Beall’s lists and/or
Scopus data, but speaking English specifically does not make much difference. Of course,
more scholars speak English than do general populations, so tentatively the key takeaway from
these figures should be that, for the most part, language does not seem to be a serious entry
barrier into predatory publications.
Language zones, in turn, reflect broader differences related to religion, culture, and history,
including past colonial links, which often translate to shared institutions and principles of
governance. Arabic countries are likely to appear, on average, highly prone to suspected
predatory publishing due to a bundle of these factors that affect how research is organized,
evaluated, and funded far more than the impact of the language itself. In any case, the language
zones are a handy tool to account for broad differences along these lines, especially because
such data are available for a very large sample of countries.
Third, it is notable that the top 20 list (Table 2) includes oil-rich countries such as Brunei,
Iraq, Kazakhstan, Libya, Nigeria, and Oman, and a closer look at the data reveals that a few
more, including Algeria, Bahrain, Iran, Russia, and Saudi Arabia, line up just short of the top
20. Why could there be a connection between oil riches and susceptibility to predatory pub-
lishing at the national level? In countries that benefit from oil-related revenues, the fiscal
constraints of the governments are eased, so that they can spend on whatever suits them,
10 More detailed stratification, such as dividing Asia into South, East, Central, and West, or Africa into North
and Sub-Saharan, runs into the problem of too few countries in some subgroups, which would make aver-
ages unreliable.
11 For example, there are four countries in which both English and French are spoken by at least 20% of the
population (Canada, Cameroon, Israel, and Lebanon). Nevertheless, the vast majority of countries are
assigned to a single language zone.
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including the support of academic research, more than other otherwise similar countries. It
may not be coincidental that some of the oil-rich countries, particularly in the Middle East
but also elsewhere (Sarant, 2016; Schmoch, Fardoun, & Mashat, 2016), began to invest their
resource windfalls in developing indigenous university sectors, while lacking a strong
research evaluation culture, which takes time to develop. Although this strategy could be
beneficial for the long-term development of these countries, if fortunes are perhaps hastily
poured into supporting research, there could be undesirable side effects, such as a spike in
predatory publishing.
To check whether there is a systematic pattern, we draw on indicators for rents from natural
resources in the World Development Indicators database (World Bank, 2018), specifically
from oil and natural gas, and also for a comparison of rents from other resources, including
coal, minerals, and forests. Countries are classified as intensive on the respective resources if
their resource rents account for more than 5% of GDP; this may sound low, but in practice
constitutes a healthy boost to the government budget. The results confirm that countries with
an economy intensive on rents from oil and natural gas are on average noticeably more sus-
ceptible to suspected predatory publishing than the rest of the world. Moreover, interestingly,
this seems to be specific to oil and natural gas, as countries rich in other types of natural
resources display even less tendency to this kind of publishing than countries that are not
particularly endowed with any of the natural resources considered here.
Fourth, we examine whether there are differences along the level of economic develop-
ment. For this purpose, we use the World Bank (2016) classification that divides countries into
four groups according to gross national income per capita. In line with the anecdotal evidence
discussed above, high- and low-income countries appear to be the least affected12. The worst
situation is in middle income countries, many of which recognize the role of research for
development, and therefore strive to upgrade, but lag significantly behind advanced countries
not only in technology but also in their ability to effectively evaluate and govern their emerging
research systems. Yet the largest difference in the proclivity to suspected predatory publishing
is between lower middle-income countries, such as Indonesia, India, and the Philippines, and
low-income countries. Overall, therefore, there seems to be a nonlinear, specifically inverse
U-shaped, relationship.
Finally, as already mentioned above, the low tendency towards suspected predatory pub-
lishing in low-income (the least developed) countries may be related to the small size of their
public research sectors. To examine whether size matters, we divide the sample into quartiles
according to the total number of articles published. Countries with small research sectors do
not fall into the most frequent contributors to suspected predatory journals, with the single
exception of Tajikistan. In fact, their vast majority rank well below the world average. More
than half of low-income countries indeed fall into the small size category, and thus it is not
surprising that the propensity to suspected predatory publishing proves to be similarly low in
both country groups. Again, there seems to be an inverse U-shaped relationship, albeit with a
different shape of the distribution.
Next, results are reported by the source list we used to identify predatory journals using
three categories: Beall’s list of standalone journals; Beall’s list of publishers excluding
12 The high-income group includes Persian Gulf countries, namely Bahrain, Kuwait, Oman, Qatar, Saudi
Arabia, and United Arab Emirates, which are rich primarily thanks to oil drilling in the region and in which,
with the exception of Qatar, the propensity to suspected predatory publishing is significantly above the world
average. If these countries are excluded, the average propensity to suspected predatory publishing in the
high-income group drops further to 1.74%.
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Frontiers; and Frontiers. The latter is analyzed separately to account for the controversy
surrounding the inclusion of Frontiers Research Foundation on Beall’s list of publishers, as
already discussed above. Frontiers does exhibit a noticeably different pattern from the other
two sources. Authors publishing in Frontiers journals are distributed far more evenly across the
country groups and in some respects, such as along income per capita, even display an oppo-
site tendency compared to the other sources lists. The top 20 list of countries with the highest
propensities to publish in Frontiers journals features Austria, Switzerland, Netherlands,
Belgium, Germany, and Israel, and in these as well as most other advanced countries Frontiers
is the dominant source in the total figures13. As a result, the main patterns identified above are
even more pronounced in the total figures excluding Frontiers. From this perspective, Frontiers
truly does not look like a typical predatory publisher.
The absolute numbers of articles in suspected predatory journals are also worthy of consid-
eration. In countries with large research systems, predatory publishing can be quite extensive,
even if the proportion relative to the total number articles does not seem problematic. The
main case in point is China, which does not stand out in relative terms, with 3.66% of sus-
pected predatory journal articles in the total national article count, but around 44,000 articles
published in suspected predatory journals had at least one coauthor from China; this is by far
the largest number worldwide. This means that nearly every fourth suspected predatory journal
article has a Chinese coauthor. Next are India and the United States, with almost every sixth
and ninth suspected predatory journal article coauthored by a researcher from that country,
respectively. In these countries, there are legions of researchers who are willing to pay to have
their work published in suspected predatory journals.
Table 4 provides details of the top 20 most affected countries and the averages across all
countries by field of research. The latter indicate that the worldwide propensity to publish in
suspected predatory journals is almost two times higher in Social and Life Sciences than in
Health and Physical Sciences. Social Sciences are particularly ravaged by this problem in a
number of countries: In seven countries, including the relatively large research systems of
Malaysia, Indonesia, and Ukraine, more than one fifth of articles appear in suspected preda-
tory journals, and in 14 countries more than one tenth of articles fall into this category. Argu-
ably, the credibility of the whole field is at stake here.
Indonesia, Iraq, and Oman feature on the top 20 lists in all four fields and Egypt, Iran,
Kazakhstan, Libya, Malaysia, Nigeria, Palestine, Sudan, and Yemen in three. In these countries,
predatory publication practices may have become a systemic problem at the national level, not
limited to particular clusters. In contrast, and perhaps even more interestingly at this point, there
are countries in which only specific fields have gone rogue. For example, China is by far the
worst in Health Sciences, but does not appear on any other field list14. Albania stands out in
Social Sciences only. Likewise, India only looks disreputable in Life and Physical Sciences,
Russia in Life and Social Sciences, and Ukraine in Social Sciences15.
13 Approximately two-thirds of suspected predatory journal articles from advanced countries are published by
Frontiers. South Korea is a major outlier among advanced countries, not only because of its high overall
penetration of this kind of publishing but also in the fact that the vast majority of these articles are not in
Frontiers journals. Taiwan and Slovakia are similar but to a lesser degree.
14 Nevertheless, one must not forget the caveat repeatedly mentioned above that the data predominantly
includes journals published in English. China not only has a different language but also its own writing sys-
tem; thus local problems with the predatory model of publication may largely escape our attention.
15 In general, there are far more former socialist countries, especially former members of the Soviet Union, on
the top 20 list in Social Sciences than in other fields. Social Sciences were particularly isolated, indoctri-
nated, and devastated during the communist era, so it is not surprising that this is the case.
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Table 4.
Percentage of suspected predatory journal articles in total articles by field of research, top 20 countries 2015–2017
Health Sciences
Life Sciences
Physical Sciences
Social Sciences
China
Libya
Taiwan
Egypt
South Korea
Algeria
Luxembourg
Suriname
Saudi Arabia
Nigeria
Iraq
Palestine
Indonesia
Sudan
Iran
Malaysia
Chile
Italy
United Arab Emirates
Oman
All countries
11.72
Kazakhstan
6.20
4.87
4.84
4.73
4.58
4.57
4.55
4.54
4.48
4.36
4.13
4.05
4.01
3.83
3.79
3.76
3.63
3.62
3.56
1.98
Iraq
Syria
India
Algeria
Egypt
Togo
Palestine
Libya
Indonesia
Nigeria
Oman
Morocco
Sudan
Iran
Russia
Yemen
Macedonia
Niger
Mauritania
All countries
28.10
16.55
14.29
13.59
10.99
10.94
10.37
10.09
9.39
9.11
9.10
8.77
8.42
7.91
6.93
6.61
6.49
6.19
6.02
6.00
3.39
Indonesia
Malaysia
Philippines
Iraq
Jordan
India
Yemen
Sudan
Morocco
Oman
South Korea
Kazakhstan
Bahrain
Liberia
Palestine
Nigeria
Brunei
Egypt
Saudi Arabia
Libya
All countries
22.31
11.77
10.90
10.66
9.19
8.65
8.36
8.05
7.86
7.70
7.54
7.17
6.70
6.45
6.31
6.31
5.96
4.99
4.85
4.62
1.96
Albania
Malaysia
Yemen
Indonesia
Tajikistan
Ukraine
Kazakhstan
Russia
Brunei
Oman
Iraq
Azerbaijan
Iran
Syria
Thailand
Nigeria
Slovakia
Bahrain
Jordan
Kyrgyzstan
All countries
37.04
29.15
28.89
27.21
25.64
22.63
21.78
17.54
12.60
12.39
12.24
12.15
11.32
10.11
9.94
9.28
9.27
9.04
8.13
8.06
3.99
Note. Journals can be assigned to multiple fields of research. Only countries with at least 30 total articles in the respective field of research are included.
Source: Scopus (2018a), author’s calculations.
Overall, we have identified a handful of factors that seem to be relevant for explaining
cross-country differences in the propensity to suspected predatory publishing, and which
beg for more elaborate examination. Nevertheless, tabulations of the data can only get us
so far in isolating their individual effects. Due to limited space and because a combination
of several factors appears to be in play, we do not delve deeper into descriptive evidence
by field of research but rather explore these patterns using a multivariate regression framework
in the next section. The full results at the country level in total and by field of science are
available for download in Zenodo (Macháček & Srholec, 2022b)16.
16 Note that most of the patterns by country groups identified in the total data also apply by field of research, as
also vindicated by the regression results in Section 4.
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5. REGRESSION ANALYSIS
In this section, we explore the cross-country differences with the help of an econometric
model. The main focus of the analysis is on testing the hypothesized relationships between
the level of economic development measured by GDP per capita, size of the (public) research
sector measured by the total number of articles and the propensity to suspected predatory
publishing, while controlling for other relevant factors. The empirical model to be estimated
is as follows:
Yij ¼ α þ β GDPi þ γ SIZEi þ δ Xi þ μ
þ εc
j
(1)
where the outcome variable Y is the proportion of articles published in suspected predatory
journals, variously defined, GDP per capita represents the level of economic development,
SIZE represents the size of the research sector, X is the set of country-level control variables,
δ is a fixed effect for the field of research represented by respective dummies, i denotes a coun-
try, j denotes a field of research, and ε is the standard error term. Hence, the basic unit of
analysis is a field of research in a given country. Because differences between fields of research
are fully accounted for by the fixed effects, the estimated coefficients of the country-level
variables explain exclusively within-fields variability.
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The dependent variable is a proportion that falls between zero and one. The ordinary least
squares (OLS) estimator tends to produce predicted values outside of this range and assumes
linear relationships. Both problems are addressed by using a fractional logit (binomial) in the
generalized linear models (GLM) framework. Robust standard errors derived from Huber-
White sandwich estimators are reported. Only observations with at least 30 total articles in
the respective country-field and with full data available for the explanatory variables are
included in the estimation sample. As a result, the econometric analysis is limited to 630
observations in 163 countries17. All estimates are performed in Stata/MP 15.1.
Whenever possible, we use continuous variables to measure the explanatory factors, as
although the number of observations is essentially quadrupled by using the field-specific data,
the sample is still relatively small. As envisaged above, GDP per capita (PPP, constant 2011
international dollars) is used to measure the level of economic development and the total num-
ber of articles indexed in Scopus is used as a rough proxy for the size of the research sector. Oil
and natural gas rents (% of GDP) are used to control for the availability of extra resources that
ease the fiscal constraints of the governments to invest in research. Latitude and longitude of
the country’s centroid, instead of plain continental dummies, are used to account for geogra-
phy. However, the only way to control for the language zones is to use dummies. GDP per
capita and the size of research sector variables are used in logs to curtail the impact of outliers.
All variables refer to (if applicable averages over) the reference period 2015–2017. For
descriptive statistics, definitions and sources of the variables entering the regression analysis,
see Tables A1 and A2 in the Appendix.
The regression analysis is used as a descriptive tool in this paper. The purpose of the regres-
sion model is to test whether the broad cross-country patterns identified above hold in a mul-
tivariate framework, when the possible influence of other relevant factors is accounted for. It
should be emphasized that the cross-sectional nature of the data does not allow for testing of
17 Cuba, Eritrea, North Korea, Somalia, and Syria are excluded due to missing data on GDP per capita. Com-
oros, Djibouti, Timor-Leste, and Turkmenistan are eliminated because they did not generate more than 30
total articles in any of the fields of research.
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causality, the estimated relationships indicate correlations, and the results should therefore be
interpreted with caution.
Table 5 provides results for the benchmark outcome variable of total suspected predatory
publishing (Column 1), then the results are replicated separately by the source list (Columns 2–4)
and finally estimated for the total, excluding Frontiers (Column 5). As the descriptive overview
revealed that there could be a nonlinear relationship between the propensity to suspected
predatory publishing on the one hand and the level of economic development as well as
the size of the research sector on the other hand, we test for this possibility by including
the respective variables in squared terms.
GDP per capita has a significantly positive main effect, but the negative squared term
indicates that there is indeed an inverse U-shaped relationship. The results confirm that the
proclivity to suspected predatory publishing has a tendency to increase with the level of eco-
nomic development, but only up to a point, after which the relationship turns negative. Hence,
countries at a medium level of development are the most vulnerable. Likewise, the size of the
research sector comes out with a significantly positive main effect and a negative squared
term; thus the same interpretation applies, albeit the relationship is estimated to be far less
curvilinear18.
Some of the control variables prove to have even more statistically significant coefficients.
First, more reliance on oil and natural gas rents, which, ceteris paribus, loosens the fiscal con-
straints of governments, is strongly positively associated with suspected predatory publishing.
Of course, this is not to say that such resources should not be used to fund research, but there
is a catch. Second, Arabic countries are confirmed to be particularly prone to suspected pred-
atory publishing, even after oil and natural gas rents and other factors are accounted for, so
there is something special about this area. Further, English is assumed to primarily control for
the suspected language bias of Beall’s lists and Scopus, but this worry is not supported by the
results. Finally, longitude has a significantly positive coefficient, so being farther east of the
Greenwich meridian implies higher inclinations towards suspected predatory publication.
As far as the comparison by source list is concerned, the results confirm that Frontiers has a
different modus operandi than the rest of the pack. If only articles in Frontiers journals are con-
sidered, for instance, GDP per capita has statistically significant but opposite signs from the
benchmark results. In fact, the model explains this outcome variable quite poorly, from which
it follows that a different approach is needed to get to the bottom of what is up with this publisher.
Although there is no evidence in the data presented upon which we can judge whether the inclu-
sion of Frontiers on Beall’s list was justified or not, the results at the very least clearly indicate that
Frontiers is atypical. Henceforth, therefore, we focus on the outcomes excluding Frontiers19.
Figure 2 gives graphical representations of the estimated relationships of main interest,
which provide a handy platform for discussing the results in more detail. The figures clearly
illustrate that these relationships follow an inverse U-shaped curve. The propensity to sus-
pected predatory publishing increases with GDP per capita up to approximately the level of
countries such as India, Nigeria, and Pakistan, after which, however, there is a steep decline.
Along the size measure there is initially a steady increase of suspected predatory publishing
until a turning point at the level of countries with relatively large research systems such as
18 If the squared terms are excluded from the model, both coefficients come out highly statistically significant,
but GDP per capita has a negative sign while the size of research sector has a positive sign.
19 It needs to be emphasized that the authors of this article have never had any connection to the Frontiers
Research Foundation or any of their journals in any capacity.
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Table 5.
Explaining propensity to suspected predatory publishing; GLM with logit link for binomial family, 2015–2017
Constant
GDP per capita
(1)
Total
−6.405***
(2)
Standalone
−11.227***
(3)
Publishers excl. Frontiers
−7.690***
(4)
Frontiers
−5.991***
(5)
Total excl. Frontiers
−7.936***
(0.877)
0.308*
(0.182)
(1.941)
0.838***
(0.284)
(1.393)
0.450
(0.285)
(0.778)
−0.301*
(0.158)
(1.270)
0.535**
(0.255)
GDP per capita squared
−0.100***
−0.296***
−0.149***
0.113***
−0.180***
Size of the research sector
0.405**
1.042**
(0.034)
(0.068)
Size of the research sector squared
(0.188)
−0.017*
(0.408)
−0.050**
(0.010)
(0.021)
(0.054)
0.446
(0.298)
−0.019
(0.016)
Oil and natural gas
0.019***
0.027***
0.023***
English spoken
French spoken
Spanish spoken
(0.005)
(0.007)
−0.095
(0.115)
−0.088
(0.119)
−0.145
−0.171
(0.179)
−0.321
(0.234)
−0.544
(0.188)
(0.408)
(0.007)
−0.190
(0.178)
−0.179
(0.178)
−0.481
(0.323)
Arabic spoken
0.532***
0.648**
0.681***
Latitude
(0.175)
(0.258)
0.003
0.013**
(0.003)
(0.006)
(0.215)
0.001
(0.004)
Longitude
0.005***
0.005***
0.008***
Field of research
Included
Included
(0.001)
(0.002)
AIC
BIC
Number of research fields
Number of countries
Number of observations
153.03
219.72
4
163
630
60.38
127.07
4
163
630
(0.002)
Included
108.63
175.32
4
163
630
(0.027)
0.174
(0.178)
−0.005
(0.009)
−0.011*
(0.006)
0.022
(0.114)
0.245**
(0.106)
0.246
(0.180)
0.102
(0.124)
−0.001
(0.002)
−0.001
(0.001)
Included
70.63
137.32
4
163
630
(0.049)
0.588**
(0.272)
−0.027*
(0.015)
0.024***
(0.007)
−0.183
(0.157)
−0.215
(0.173)
−0.498*
(0.280)
0.686***
(0.209)
0.003
(0.001)
0.007***
(0.001)
Included
125.86
192.55
4
163
630
Note. Only countries with at least 30 total articles in the respective field of research are included. The dependent variable is the proportion of suspected
predatory journal articles in total articles. Robust standard errors are in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% levels.
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Figure 2. Estimated effects of GDP per capita (upper panel) and size of the research sector (lower
panel) on the propensity to suspected predatory publishing (total excluding Frontiers); GLM with
logit link for binomial family, 2015–2017. Note. Based on results in Column 5 of Table 5. Predictive
margins with 90% confidence intervals are displayed.
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Malaysia and Saudi Arabia, which is followed by only a slight decrease for the largest ones.
The overlapping confidence intervals indicate that, for GDP per capita, the relationship differs
most significantly between medium and highly developed countries, while for the size
measure the difference is mainly between small and medium research sectors. So what does
this mean?
GDP per capita is used for a lack of better measurements that are more intimately related to
how a research system is organized and that would be available for a broad sample of coun-
tries, including many developing ones. Nevertheless, GDP per capita tends to be highly cor-
related to many other salient measures. What is likely to make the key difference between
medium and highly developed countries that drives the results presented in this study is the
capability to perform meaningful research evaluation, including advanced scientometrics and
peer review of the actual content of published papers, that does not fall back on only counting
the number of articles indexed in Scopus or elsewhere, regardless of quality and merit. If the
government is not able to set the right mix of incentives to the public research sector, which is
arguably very difficult even in advanced countries, those who do not shy away from predatory
publishing have free rein.
Size is an important consideration, as noted above, because large research systems are
more complex and therefore notoriously more difficult for governments to evaluate, manage,
and steer than small systems. If two countries maintain equally primitive research evaluation
frameworks, one with a large research sector composed of dozens of diverse institutions will
tend to be more susceptible to predatory publishing than one with a tiny research sector com-
posed of perhaps only a few easy-to-oversee workplaces. Large research systems suffer from a
certain degree of anonymity, blind spots, and dark corners, in which predatory publishing
flourishes. Around the turning point, however, the system becomes large enough to warrant
investment in advanced research evaluation capabilities, which makes life more difficult for
those exploiting the loopholes, so that the relationship between predatory publishing and size
flattens and even curves slightly down.
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6. CONCLUSIONS
Taken at face value, the evidence presented in this paper indicates that countries at a medium
level of economic development and with large research sectors are most prone to publishing
in suspected predatory journals. This should be a dire warning for developing countries that
devote large resources to support research but which may not pay sufficient attention to
upgrading their research governance capabilities, including their research evaluation frame-
work. Moreover, the evidence suggests that oil-rich and/or Arabic and/or eastern countries
tend to be particularly vulnerable, which completes the picture of who should be primarily
on the lookout for predators.
Nevertheless, the general patterns are from a bird’s-eye view, so there are exceptions driven
by idiosyncratic factors. The prime example of an outlier appears to be Albania, which does
not feature most of the high-risk characteristics but is still among the most affected countries.
Predatory publishing is a truly global phenomenon, from which no emerging research system
is entirely safe. Policy makers in developing countries that do not fit the description of the main
risk group should not be fooled into thinking that the problem does not concern them, because
if they flinch in their vigilance, their homeland may end up on the list of the most affected
countries next time.
The results are broadly in line with previous estimates by Shen and Björk (2015), Xia et al.
(2015), and Demir (2018), as well as Wallace and Perri (2018), in the sense that Asia and North
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Africa provide the most fertile grounds for predatory publishing and that in particular India and
Nigeria belong to the main sources. However, this paper has not only gathered one of the most
comprehensive databases of suspected predatory journals, and used far more complete
evidence than previous studies, but also provided a much higher level of granularity on the
cross-country differences. In fact, a number of countries not mentioned in previous studies are
shown here to be likely to suffer substantially from the problem of predatory publishing. In
addition, this paper is the first to study the cross-country differences systematically in an
econometric framework.
A major limitation of this study is that we can only speculate that the way in which research
is evaluated in each country makes the primary difference, whether this includes research
organizations at the national level, projects supported by funding agencies, and/or even indi-
viduals working on career progression. Ideally, we would like to take the characteristics of the
research evaluation framework directly into account, including whether evaluation primarily
concerns quantity or quality, whether formulae based on quantitative metrics are used, how
advanced the underlying bibliometric approach is, whether insights from peer review assess-
ment are factored in, and, consequently, what principles are applied when allocating research
funding. Unfortunately, indicators of this kind are not available for more than a handful of
advanced countries, which are not the most relevant here. To pin down the impact of these
factors on the propensity to predatory publishing remains an important challenge for future
research on this topic.
Another limitation is the cross-sectional nature of the analysis, which, as explained above,
stems from the fact that historical data is not reliable. Longitudinal data would allow for more
elaborate tests, particularly with respect to causality, than those employed in this paper. There
are also likely to be lags in the cause–effect relationships that could be detected when long
time series become available. In any case, the 3-year period studied here is rather short, as
predatory publishing is a relatively recent and fairly dynamic phenomenon. This may have
influenced the results and the list of most affected countries may look somewhat different if
a similar exercise is repeated in a few years, which would be desirable.
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It should be stressed that the results of this paper should not be interpreted to mean that
developing countries should invest less in research, because this would undermine their
emerging and often fragile national innovation systems and ultimately thwart productivity
growth (Fagerberg & Srholec, 2009). However, it is fair to issue a cautionary note that preda-
tory publishing has the potential to complicate research evaluation and therefore effective allo-
cation of research funding greatly in many corners of the world. Developing countries aiming
to embark on a technological catch-up trajectory need to take these intricacies more seriously
than ever.
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Scopus needs to stay focused on discontinuing coverage of questionable journals and most
importantly step up efforts to prevent their indexing in the first place, as once they are allowed
in, the content they publish before their potential discontinuation remains in the database
forever. Scopus should strive to find a way to fact check whether the journal adheres to the
declared editorial practices, most prominently how the peer-review process is performed in
practice. It should possibly engage the research community more actively with regard to col-
lecting data on complaints about publication standards in both currently and prospectively
indexed journals. Unless the selection criteria are upgraded to reflect not only the declared
policy but also reality on the ground and/or the bar for inclusion in terms of bibliometric cri-
teria is raised significantly, new questionable journals will keep creeping in to the database,
including rebranded and transformed business operations that have been flagged as predatory
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in the past. In the meantime, evaluators, research managers, or university rankings that use
Scopus data as inputs in their decisions need to be mindful about it.
Last, but not least, as already discussed above, Beall’s lists no doubt have limitations.
Beside the lack of transparency of the decisions to list some of the journals and publishers,
the most obvious one has become the fact that Jeffrey Beall stopped curating the lists under
his name at the beginning of 2017 (Straumsheim, 2017), as the result of which the data have
become gradually outdated. Even though his lists continue to be maintained by someone on a
new website (Anonymous, 2022), their updates have not been authorized, which makes their
use problematic for research purposes. Future research on more recent evidence on this topic
ought to look for different data sources. Cabells Predatory Reports, which have been devel-
oped in the meantime, seem promising (Cabells, 2022), but this list is proprietary and locked
behind a paywall, and the database as a whole is not easily available20. Clearly, the research
community needs to continue efforts to improve identification, measurement, and understand-
ing of the problem of predatory publishing.
ACKNOWLEDGMENTS
Earlier versions of the paper were presented at the IDEA think-tank seminar Predatory Journals
in Scopus, Prague, November 16, 2016, the Scopus Content Selection and Advisory Board
Meeting, Prague, November 3, 2017, and the 17th International Conference on Scientometrics
& Infometrics, Rome, September 2–9, 2019. We thank the participants at these events as well
as Ludo Waltman and Vincent Larivière, editors of Quantitative Science Studies, for their useful
comments and suggestions. Martin Srholec also thanks his beloved wife Joanna for her support
of the preparation of a revised version of the manuscript during the heat of the Covid-19 crisis.
All the usual caveats apply.
The paper was first published in Scientometrics in February 2021 (Macháček & Srholec,
2021). Following pressure by Frontiers, the Editor-in-Chief of Scientometrics decided to retract
the paper based on dubious claims that some of the findings are unreliable (Macháček &
Srholec, 2022a). We refuted these claims and disagreed with this decision. The retraction
was also strongly condemned by prominent members of the scientometric research commu-
nity (Retraction Watch, 2021; Srholec, 2021). Later on, Akadémiai Kiadó and Springer
Nature—the owner and publisher of Scientometrics, respectively—reverted to us the rights
to publish the paper. The paper has undergone a minor revision before its republication in
Quantitative Science Studies, mostly by extending some of the discussion and reflecting on
the most recent development in this line of research but not by addressing the alleged flaws
that were used to justify the retraction.
AUTHOR CONTRIBUTIONS
Vít Macháček: Conceptualization; Data curation; Methodology; Software; Validation; Visualiza-
tion; Writing—original draft. Martin Srholec: Conceptualization; Formal analysis; Funding acqui-
sition; Methodology; Validation; Visualization; Writing—original draft; Writing—review & editing.
Both authors contributed equally to this work.
20 For example, in July 2021, we discussed with Cabells the possibility of obtaining access to an early version of
the list that underlies their Predatory Reports for the purpose of running a replication study with regard to
evidence presented in this paper and comparing the results. After a detailed discussion, however, Cabells
concluded that this would not be desirable to do and the access has not been granted to us.
Quantitative Science Studies
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COMPETING INTERESTS
The authors have no competing interests.
FUNDING INFORMATION
Financial support from the Czech Academy of Sciences for the R&D&I Analytical Centre
(RaDIAC) and from the Czech Science Foundation (GAČR) project 17-09265S is gratefully
acknowledged.
DATA AVAILABILITY
Data files that provide a list of journals linked to Beall’s lists indexed in Scopus (22_QSS_
MachacekSrholec_Supp1.xlsx) and country-level data (22_QSS_MachacekSrholec_Supp2.
xlsx) are available in Zenodo (Macháček & Srholec, 2022b). Scopus data for individual jour-
nals are proprietary and hence cannot be made available for legal reasons; these data can be
accessed directly from https://www.scopus.com.
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Quantitative Science Studies
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Predatory publishing in Scopus
APPENDIX
Table A1. Descriptive statistics of the variables, 2015–2017
Mean
St. dev.
Min
Max
N
Dependent variables:
Total
Standalone
Publishers excl. Frontiers
Frontiers
Total excl. Frontiers
Explanatory variables:
GDP per capita
Size of the research sector
Oil and natural gas
English spoken
French spoken
Spanish spoken
Arabic spoken
Latitude
Longitude
0.028
0.005
0.016
0.007
0.021
2.341
8.355
2.676
0.221
0.129
0.123
0.117
0.039
0.013
0.033
0.009
0.039
1.211
2.323
7.124
0.416
0.336
0.329
0.322
0
0
0
0
0
−0.443
3.989
0
0
0
0
0
0.370
0.216
0.370
0.057
0.370
4.773
14.071
48.318
1
1
1
1
20.454
24.752
−41.814
67.470
20.439
58.960
−112.10
177.97
630
630
630
630
630
163
163
163
163
163
163
163
163
163
Note. GDP per capita and the size of research sector in logs. N – number of observations.
Quantitative Science Studies
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Variable
Predatory journal
articles
Table A2. Definition and sources of the variables
Definition
The proportion of articles in journals linked to Beall’s lists by authors from the respective
country in total articles from that country recorded in the Scopus database.
Source
Scopus (2018a)
GDP per capita
Gross domestic product (GDP) converted to constant 2011 international dollars using
World Bank (2018)
purchasing power parity (PPP) rates.
Size of the
research sector
Counts of total articles by authors from the respective country recorded in the Scopus database.
Scopus (2018a)
Oil and natural gas
The difference between the value of crude oil and natural gas production at regional prices
World Bank (2018)
and total costs of production as percentage of GDP.
English spoken
Dummy with the value 1 if more than 20% of population speaks English.
French spoken
Dummy with the value 1 if more than 20% of population speaks French.
Spanish spoken
Dummy with the value 1 if more than 20% of population speaks Spanish.
Arabic spoken
Dummy with the value 1 if more than 20% of population speaks Arabic.
Mayer and Zignago (2011)
Mayer and Zignago (2011)
Mayer and Zignago (2011)
Mayer and Zignago (2011)
Latitude
Latitude of country centroid measured in degrees from the equator, with positive values going
Gallup et al. (1999)
north and negative values going south.
Longitude
Longitude of country centroid measured in degrees from the Prime Meridian with positive
Gallup et al. (1999)
values going east and negative values going west.
8
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