RESEARCH ARTICLE
Mapping and impact assessment of
phenomenon-oriented research fields:
The example of migration research
Liane Rothenberger1
, Muhammad Qasim Pasta2
, and Daniel Mayerhoffer3
1Catholic University of Eichstätt-Ingolstadt, Ostenstraße 25, D-85072 Eichstätt, Deutschland
2Usman Institute of Technology, Karachi, Pakistan
3Institute for Political Science, University of Bamberg
Schlüsselwörter: citations, collaboration network, impact factor, migration studies, MigCite, MigImp
ABSTRAKT
Research that is not explicitly bound to a distinct discipline has not yet gained much
acknowledgment with regard to research impact assessment and mapping of the respective
research field. In diesem Artikel, we provide a suggestion for new impact metrics taking the example
of migration research as a phenomenon-oriented research field. Therewith, research merit is
made comparable and is calculated irrespective of discipline. We show how the field of
migration studies evolved, apply our new metrics and give insight into impact factors, Zahlen
of citations of articles, and authors, as well as journals. Weiter, we present a field-related
collaboration network that indicates a rather disconnected community. Jedoch, collaborations
between researchers are on the rise. In our conclusion, we argue that there is a need for
further assessment of research impact within other phenomenon-oriented research fields.
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1.
EINFÜHRUNG
Scientific knowledge graphs (SKG) and the impact of research have been built and measured for
various disciplines (z.B., Kajikawa, Ohno et al. (2007) for sustainability science, Marshakova
(1981) for information science, and Popp, Kovács et al. (2016) for agricultural economics). Big
impact assessors such as the SCImago ranking portal furnish researchers from all over the world
who work in (sub-)disciplines both big and small with numbers and measurements such as impact
factors (for journals) and h-indices (for individual researchers or groups of researchers). The Sci-
ence Journal Ranking (SJR) indicator “represents a bibliometric measure, based on a diffusion
Algorithmus, for the quantification of the prestige of scientific journals” (Radicchi, Fortunato, &
Vespignani, 2012, P. 250), relying on Elsevier’s Scopus database. Citation counts allow for an
“evaluative measure of the scientific productivity of authors and the status of scientific journals”
(Marshakova, 1981, P. 13), assuming that “citations represent a proxy for the quantification of
scientific relevance” (Radicchi et al., 2012, P. 251; see also Moed, 2005). Jedoch,
phenomenon-related research receives much less attention and some areas are entirely neglected.
“Citation analysis has already been gaining popularity in information science and in the science
of science” noted Marshakova (1981, P. 13) four decades ago. Using citation counts we decide
about the relevance and quality (though somewhat falsely) of a paper and its author(S). The “link
between two papers as the number of identical documents cited by the two papers” (Marshakova,
1981, P. 21) is called bibliographic coupling, whereas “the co-citation method (or prospective
Keine offenen Zugänge
Tagebuch
Zitat: Rothenberger, L., Pasta, M. Q.,
& Mayerhoffer, D. (2021). Mapping and
impact assessment of phenomenon-
oriented research fields: The example
of migration research. Quantitative
Science Studies, 2(4), 1466–1485.
https://doi.org/10.1162/qss_a_00163
DOI:
https://doi.org/10.1162/qss_a_00163
Korrespondierender Autor:
Liane Rothenberger
rothenberger@ku.de
Urheberrechte ©: © 2021 Liane Rothenberger,
Muhammad Qasim Pasta, and Daniel
Mayerhoffer. Published under a
Creative Commons Attribution 4.0
International (CC BY 4.0) Lizenz.
Die MIT-Presse
Mapping and impact assessment of phenomenon-oriented research fields
coupling) unites the papers cited by the same documents” (Marshakova, 1981, P. 21). Cocitation
analysis was introduced by Small (1973) and has gained importance since then. It is by citing a
colleague, as well as by collaborating with them, that networks develop in the sense that a
(formal) relation between one author and another is established as they include the paper in their
arbeiten, thereby showing a connection to or similar interest in the same research topic. Granovetter
(1973, P. 1377) claims that such network analyses highlight micro-macro linkages in that they
show how individuals are “bound up with larger-scale aspects of social structure.” The status of
an actor can then be described by their centrality in the network. These analyses, zu, often dis-
regard research that is not directly affiliated with a distinct discipline.
Our article has four main sections. By using the field of migration research, we first make an
argument that measuring research impact for phenomenon-oriented research fields that do not
appear in the well-known indices is relevant and explain why a different measurement is
erforderlich. Außerdem, we provide a short insight into the field of migration research. Zweite,
we set up a framework describing how to acknowledge scientific merit in this area by suggest-
ing a new calculation for research impact assessment for internally diverse fields. Dritte, Wir
provide a broad range of citation and collaboration measures and SKGs to give an overview so
that we can select the most relevant measures for building a novel sophisticated citation factor.
We then, lastly, discuss strengths and limitations of our work and obstacles we encountered,
before giving an outlook on future research in this area.
With regard to the SCImago rankings (vgl. https://www.scimagojr.com/), the options offered on
the website let the researcher sort results according to disciplines (such as chemistry, dentistry,
and engineering) and according to subject categories (such as small animals, pollution, horticul-
tur, and ocean engineering). Jedoch, this is of no use for migration researchers: Their field is
multidisziplinär, with researchers coming from ethnology, political science, communication
studies and many others; Plus, the subject of migration is not included in the subject list, vielleicht
because it is a comparatively novel topic. Migration research belongs to what Schwarz and
Stensaker (2016) call “PDR” (phenomenon-driven research). This research does not exclusively
belong to a specific discipline but “is problem-oriented research that focuses on capturing,
documenting, and conceptualizing an observed phenomenon of interest in order to facilitate
knowledge creation and advancement” (Schwarz & Stensaker, 2016, P. 245). This leads us to
ask the following research question: How can we measure and visualize research impact in
phenomenon-oriented research fields? This article aims to provide an answer to this question.
1.1. Relevance
In the broader field of the science of science (Fortunato, Bergstrom et al., 2018) most big biblio-
metric studies concentrate on the major disciplines (z.B., Redner (2005) for physics) that often
provide trustable databases from which to retrieve relevant information such as authors’ names,
affiliation, keywords, abstracts, and citation lists. Bedauerlicherweise, a field such as migration research
does not have its own database that lists topic-related contributions (such as the CMMC [Com-
munication and Mass Media Complete] for communication research or the APS database for
Physik). Außerdem, the number of papers as well as the citations and impact factors vary
greatly across disciplines. They reach their largest numbers, Zum Beispiel, in internal medicine
and biochemistry but are lowest in disciplines such as literature and dance (Patience, Patience
et al., 2017). If the science of science wants to step beyond the provision of SKGs and research
impact assessment for single disciplines, we are in need of fine-grained, context-sensitive repre-
sentations of scholarly knowledge in these inter- and multidisciplinary fields. This is why we want
to provide a framework of how to better conceptualize scholarly knowledge and research impact
assessment by using the field of migration research as an appropriate example.
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Mapping and impact assessment of phenomenon-oriented research fields
Due to a lack of ready-made data, we will utilize different metrics to allow us to better con-
trol for the various disciplinary backgrounds. Only then will it be possible to compare research
impact beyond discipline- and journal-bound impact factors and to generate synergies
between currently divided research communities. Jedoch, we do not propose our approach
as a substitute but rather as a valuable complement to hitherto existing impact assessment.
With regard to funding, new indicators seem relevant, as these phenomenon-oriented research
communities lack “advocates” in the funding institutions that still organize decision processes
according to distinct disciplines. A law from Radicchi, Fortunato, and Castellano (2008) says
that if “the number of citations of a paper is divided by the average number of citations col-
lected by papers in the same discipline and year, the distribution of the resulting score is essen-
tially indistinguishable for all disciplines.” However, what if there is no concrete “discipline”?
And how, zum Beispiel, can a political scientist or psychologist merit a great impact in migra-
tion research when in their core discipline migration researchers are viewed as a peripheral
Gruppe? Somit, one needs to pay attention to field-specific evolvements. This is why in the
following section we will give an insight into how migration research developed, but we will
keep this section short as the focus of this article lies in developing a general approach to
measuring research impact in phenomenon-oriented research. In the results section we present
a basic overview using descriptive statistics, then turn to the most influential journals, authors,
institutions, and countries before presenting collaboration networks. We believe that looking
at domain-specific figures other than those that are only citation-related (e.g. dominance of
Länder) can be useful for validation purposes of the whole approach.
1.2. The Field of Migration Studies
The field of migration research covers diverse topical clusters: from mobility to development,
from education to identity, from gender to racism (vgl. Figur 1).
Even though migration and flight are “anthropological constants” (Strukelj, 2020, P. 174),
they have not yet received as much scholarly attention as other areas of research, especially in
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Figur 1. Topical relations in migration studies; generated using VOSviewer Software (van Eck &
Waltman, 2010), based on our overall sample (vgl. Abschnitt 2).
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Mapping and impact assessment of phenomenon-oriented research fields
comparison to topics in the natural sciences. This might be due to the fact that migration stud-
ies is not considered a distinct research discipline and many researchers stick to their own
disciplinary approaches when dealing with the issue of migration. Whereas some researchers
have called the late twentieth century “the age of migration” (z.B., Castles & Müller, 1993),
there is evidence that the refugee crisis in 2015 has led to an increase in scholarly interest
in the general topic of migration (Pisarevskaya, Levy et al., 2020, P. 9). Zlotnik (1998, P. 430)
defines international migrants as “people who depart from their country of origin (or citizen-
ship) to reside in another country and those who return to their country of origin after residing
abroad.” Both international migrants such as refugees, migrant workers, or marriage immi-
grants, as well as internally displaced persons, must be considered when studying migration.
Zlotnik (1998) provides an overview of migration flows starting from 1965. (Previous to 1965,
there are only scarce records.) Since then, the number of international migrants has been
constantly growing and similarly the internationalization of research in this field has also been
increasing (Pisarevskaya, Levy et al., 2020). Scholarly interest has rapidly increased since the
so-called migrant crisis in 2015; Jedoch, some of the flagship journals of the field were
founded in the 1960s (z.B., International Migration in 1961 and International Migration Review
In 1964). The field of migration studies has been multidisciplinary since its inception, beginnend
primarily in the humanities and anthropology, with more recent approaches based in the
social sciences. Natürlich, migration research is not solely published in migration studies jour-
nals; yet, the core of migration studies is found in these journals and researchers who aim to
position themselves as experts in the field strive to publish in these journals. This directly leads
us to explain how we chose an appropriate sample of journals to elucidate the development of
migration research from its infancy to present day, cutting-edge migration research.
2. METHODEN
2.1. Sampling
The SCImago metrics are based on Scopus data; this is why we suggest using Scopus as a data
source. Außerdem, the Web of Science as a direct competitor does not list as many databases
and is more oriented towards the natural sciences. As migration research has lexical overlaps
with unrelated fields (z.B., “migration distance in sea turtles” (Hays & Scott, 2013)), it is diffi-
cult to generate an appropriate corpus by only looking for migration-related keywords, für
example in article titles. Even though we acknowledge that journal-based delineations exclude
all publications relevant to the field that are published in other journals or in generalist journals
(e.g. PLOS ONE, Wissenschaft, Natur), we state with Sweileh, Wickramage et al. (2018) that search
via keywords in titles and abstracts “is known to retrieve many false positive results” because
the keywords “might be also used in other scientific disciplines such as molecular biology,
genetics, botany, and veterinary.” Hence, we opt to accumulate a list of journals relevant in
the field. daher, we relied on the work of Pisarevskaya et al. (2020), who pursued an
inductive approach to publications from 40 English-language migration journals and four book
series to conduct computer-based topic modeling in migration studies, tracing changes over
Zeit. Pisarevskaya et al. (2020) collected metadata originating from Scopus (94%) and Web of
Wissenschaft (1%), and the remaining (5%) were gathered manually, ergebend 29,844 articles.
Somit, relying only on Scopus will certainly cover the bulk of the articles (we provide the full
curated list of journals plus number of articles for each journal in a repository: https://doi.org
/10.5281/zenodo.5113133). Pisarevskaya et al. (2020) observed the period from 1985 Zu
2017, for two reasons: starting from the mid-1980s, the field of migration studies became more
International, complex and diversified; and there were more than 10 active journals and a
sufficient number of them provided abstracts. Hassan, Visvizi, and Waheed (2019), Jedoch,
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Mapping and impact assessment of phenomenon-oriented research fields
assert that the international migration debate had already started in the 1960s. Following this
Streit, Wir, zu, relied on data from 1960 until November 21, 2020.
For this study, we used the Scopus Search API as well as the Semantic Scholar API for data
extraction. There are many other options available: Microsoft Academic Graph, Elsevier ICSR
Labor, BigQuery Lab, and Crossref are among them1. We preferred the selected APIs based on
ease of reproducibility. Both APIs do not require any approval process and can be easily acces-
sible through a simple signup. The scripts used to extract data are available at https://doi.org/10
.5281/zenodo.5113133.
2.1.1. Data extraction, cleaning, and integration
We extracted the data for our corpus in two steps: Erste, from the Scopus database and then from
the Semantic Scholar database. We extracted all articles with basic information using the Scopus
Search API. The API allows the user to search all articles by specifying the journal name (verwenden
EXACTSRCTITLE). Jedoch, we found that it returns articles for other journals if the provided
journal is a subpart of any other journal name. Zum Beispiel, “IDENTITIES” can also be part of
other journal titles. We excluded all those journals in the data cleaning process. For papers from
Scopus, we were able to extract articles with titles, first author, affiliation of the first author, Und
Digital Object Identifier (DOI); but due to the unavailability of an institutional account, we were
unable to get data for other authors, abstracts, and citations. To compensate for this, we used the
Semantic Scholar API, which provides this information. We used the DOI to map the records for
both databases. Figur 2 shows our high-level architecture for data extraction.
2.1.2. Network generation
For the generation of networks, we used the data from both sources. The affiliation of a paper with
institution and country is decided through the affiliation data of the first author retrieved from the
Scopus database. The Semantic Scholar database was used for authors, publication year, citations,
and references. We used the iGraph library to generate these networks, as the library is available
in Python and R languages. Gephi software was used for visualization of most of the graphs.
We generated the following networks from the data:
(cid:129) The Collaboration Network shows who is collaborating with whom. Each node repre-
sents an author, and an edge between two authors shows that the authors have gener-
ated a collaborated work where the weight of an edge depicts the number of such
collaborations.
(cid:129) In the Citation Network each node is either an article published in the selected journals or
a paper not published in the selected journals but citing an article published in the selected
journals. Each edge from an article x to another article y represents that x cited y. Wir
generated another citation network in which we only considered articles published in
the selected journals. Somit, this network shows who is citing whom within the core-
migration journals. We call the former network extended articles and the latter base
articles.
(cid:129) The Journal Citation Network shows journals citing articles of other journals. An edge in the
network from node J1 to node J2 (representing two journals) depicts an article published in
journal J1 that has cited an article published in J2. The network also contains self-loops
showing citations from articles of a journal to articles published in the same journals. Der
weight of the edge shows the number of such citations between the two journals.
1 We are thankful to the reviewers for highlighting a number of these resources.
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Mapping and impact assessment of phenomenon-oriented research fields
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Figur 2. Architecture of data extraction and analysis.
(cid:129) The Country Citation Network is similar to the journal citation network. We generated the
country citation network in which, instead of journals, each node represents a country. Ein
edge from node C1 to node C2 shows articles published by the first author belonging to a
country C1 citing another article whose first author belongs to a country C2. The weight of
the edge shows the number of such citations between the two countries.
The use of multiple data sources also has its disadvantages. One of the major disadvantages
is the unavailability of similar data in both sources. We were able to extract 35,405 articles
from Scopus databases but out of these only 32,450 articles were available in the Semantic
Scholar database. Similar to other studies (z.B., Sweileh et al., 2018), we separated certain
types of publications such as reviews, editorials, and conference proceedings. We included
only those described as “journal articles” by the Scopus database and that included an author
and references, which resulted in 27,433 articles—the most extensive corpus yet with regard
to a quantitative analysis of authors, Papiere, and citations (to the best of our knowledge) while
avoiding false positives (i.e. articles from other fields). This big corpus also distinguishes this
study from former ones (z.B., Hassan et al., 2019) that analyzed around 12,000 articles.
We refer to these 27,433 articles as “base articles”; they build the basis for a number of
information graphs generated such as the collaboration network, citation network, and country
citation network. Noch, to generate the citation network, we also included articles not published
in the list of journals we used to extract the “base articles,” but these articles cited at least one
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Mapping and impact assessment of phenomenon-oriented research fields
paper included in the base articles; we refer to these as extended articles (Ebene 1 citations).
Jedoch, we did not include citations of the “extended articles” (Ebene 2 citations) as they may
extend beyond the rim of the field’s center.
2.2. A Novel Framework for Impact Metrics in Interdisciplinary Phenomenon-Oriented
Research Fields
Because “[T]he validity and robustness of interdisciplinarity metrics should be questioned”
(Wang & Schneider, 2020, P. 257) due to the frequent contradictory outcomes of metrics that
are meant to capture the same aspects of interdisciplinarity, our study deliberately refrains from a
straightforward application of these metrics. Stattdessen, we opt for a cautious approach and employ
SKGs and aim at “indicating’” (in the sense of Marres & de Rijcke, 2020) interdisciplinarity and
its implications in the field of migration studies. In an interdisciplinary field such as migration
Forschung, directly comparing metrics of papers or authors from different disciplinary backgrounds
may yield misleading results, because average citation counts differ per discipline. Zum Beispiel,
the most influential papers in demography, an important subfield in migration studies, are cited
19 times on average; those in general internal medicine, which is also frequently concerned with
migrants’ health, average 460 citations (Patience, Patience et al., 2017). To account for these
differences, we propose a simple metric, the MigCite-Factor that normalizes the citation count of
a migration study paper by the average citation count in the field of its (Erste) author:
MigCite
¼
D
j Paper
(cid:1)
NCite Field of First Author
Cj
Þ
D
Þ
Due to the comparison with the most influential papers in their subject field of origin, Die
resultant metrics will have low absolute values, but these values are fit for direct comparison
between different papers, authors, or journals in migration studies. We also calculate the aver-
age values for migration studies. For authors from a field whose average citation count is
below the average count of migration studies, venturing into the field may be advisable
career-wise. Darüber hinaus, collaborating with authors from fields with higher average citation
count may also be a good idea from that standpoint.
To assess the impact of a paper in the field, we propose an integrated metric that accounts
for the total number of citations as well as for the number of citations within the field and for
their fraction of the overall citations. It is calculated as the square of citations from within the
field divided by the square root of all citations of the paper:
!
2
MigImp
Þ
D
j Mig
¼ Cj
P
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Þ
j allð
Cj
Papers with a higher citation count will ceteris paribus exhibit a higher MigImp to acknowl-
edge the overall citation numbers of a paper. Gleichzeitig, the specific form of the MigImp
yields especially high values for papers with a high proportion of their citations from within
migration studies. Somit, the proposed metric emphasizes within-field citations so that those
papers are acknowledged that concentrate on furthering migration research at the cost of their
own general visibility (z.B., by providing specific theories, Methoden, or data). The Pearson Cor-
relation between the MigImp of a paper and its citation count for our corpus is 0.52 Und
between MigImp and PageRank (Chen, Xie et al., 2007; Yan & Ding, 2011) es ist 0.58. This rather
moderate correlation (explored in detail in the next section) means that our metric grasps an
additional quality of a paper beyond these approaches: It focuses specifically on the impact in
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Mapping and impact assessment of phenomenon-oriented research fields
an interdisciplinary field and can thus complement the aforementioned analyses. Zusammen
MigCite and MigImp can uncover whether the relative success of a paper/author is fueled by
its/their importance in migration studies or citation practices in the author’s field of origin.
MigCite is meant for close inspection of a given author and MigImp characterizes migration
studies as a research field. We identified the field of origin via the authors’ self-definition on
the web and if we encountered disciplinary overlaps we decided to use the field in which most
of the author’s papers were published.
3. ERGEBNISSE
3.1. Basic Overview: Articles, Citations, Journals, Papers
Publications on migration date back to the 1960s, when the journal International Migration,
the outlet publishing the fourth highest number of migration studies articles was founded.
Jedoch, in those early days, only a few articles tackled this topic, wie in der Abbildung gezeigt 3
(we are showing data since 1986 for better visualization, als 90% of the articles were published
nach 1985). Contrary to the linear growth in the number of publications over time identified by
Sweileh et al. (2018, P. 10), we find an exponential increase in the number of articles. In gen-
eral, the growth of publications mirrors the general growth of global research activities.
This growth was potentially driven by the establishment of additional influential journals,
both in terms of published articles on migration (vgl. Tisch 1) and number of citations in migra-
tion articles (vgl. Tisch 2). These journals were founded after 1960, with the majority in the
1970s and 1990s after the fall of the Iron Curtain. We observe that 34% of articles are pub-
lished in the four most prominent journals. This seems to be true also for other subdisciplines,
such as agricultural economics (Popp et al., 2016, P. 157). On average, es gibt 810 articles
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Figur 3. Number of articles, authors, and of citations over time; both normalized between 0 Und 1.
Quantitative Science Studies
1473
Mapping and impact assessment of phenomenon-oriented research fields
Tisch 1. Most influential journals in migration studies according to number of (migration) papers published
Rank
1
Zeitschrift
Journal of Ethnic and
Migration Studies
2
3
4
5
Ethnic and Racial Studies
Journal of Black Studies
Internationale Migration
International Journal of
Intercultural Relations
Total
articles
2,889
Total
citations
76,054
Avg. authors
per article
1.58
Avg. citations
per article
26.3
2,508
1,684
1,674
1,624
88,911
27,671
40,491
82,238
1.41
1.38
1.63
2.39
35.5
16.4
24.2
50.6
Total
MigImp
1,788.0
1,042.0
48.1
505.0
430.0
Avg. MigImp
per article
0.619
0.416
0.028
0.302
0.265
published each year, with a maximum of 1,562 In 2017. Das, Natürlich, depends on the fre-
quency of journal issues per year and the average number of articles integrated into each issue
and volume. Citations mirror the exponential development in published papers, delayed by
about two decades—this delay becomes shorter and shorter as the numbers of citations and
the number of papers keep rising.
Tisch 1 zeigt, dass, zum Beispiel, the articles that appeared in the International Journal of
Intercultural Relations are often cited (in general), but that their impact within the distinct field
of migration research is not as high, Zum Beispiel, as the MigImp of the Journal of Ethnic and
Migration Studies.
Gesamt, our data shows that the field is growing rapidly, not only because of general
advances in academia, but moreover potentially caused by the rising number of migrants
(Sweileh et al., 2018), a rising public and political awareness of migration issues, soaring glob-
alization, and international relations. Daher, migration papers receive comparatively great
attention, as evidenced by their citations. NCite(Migration), which is the average citations of
the 31st to 500th most cited papers, as proposed by Patience et al. (2017), Ist 322.97, wohingegen
the overall average of citations in migration studies is 30.28 and the average of the second and
third quartiles in citation ranks is 12.70.
Tisch 3 lists the most cited papers: The work from 1993 topping that list may have inspired
future research on migration that is mirrored in the other influential papers published in the
Tisch 2. Most influential journals in migration studies according to average number of citations per article
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Rank
1
Zeitschrift
Population and Development
Rezension
Total
articles
1,050
Total
citations
93,155
Avg. authors
per article
1.82
Avg. citations
per article
88.72
2
3
4
5
International Migration Review
1,146
77,207
Journal of Studies in
International Education
International Journal of
Intercultural Relations
539
28,994
1,624
82,238
Cultural Diversity and Ethnic
934
43,474
Minority Psychology
1.74
1.82
2.39
3.53
Total
MigImp
150.0
1,021.0
67.5
Avg. MigImp
per article
0.143
0.891
0.125
67.37
53.79
50.64
430.0
0.265
46.55
68.9
0.073
Quantitative Science Studies
1474
Mapping and impact assessment of phenomenon-oriented research fields
Rank
1
Title
Theories of international migration: A review and appraisal
Year
1993
First Author
D. S. Massey
Total
citations
3,710
MigCite
83.33
MigImp
23.77
Tisch 3. Most cited papers
2
3
4
5
6
7
8
9
Whose culture has capital? A critical race theory discussion
2005
T. J. Yosso
3,203
72.80
2.20
of community cultural wealth
Super-diversity and its implications
2007
S. Vertovec
The Internationalization of higher education: Motivations
2007
P. Altbach
2,872
2,152
87.03
22.64
48.91
2.08
and realities
Gender and nation
1997
N. Yuval-Davis
2,146
Acculturation: Living successfully in two cultures
2005
J. Berry
The study of transnationalism: Pitfalls and promise of
1999
A. Portes
an emergent research field
2,145
1,894
51.10
26.48
4.29
6.60
45.09
45.01
Conceptualizing simultaneity: A transnational social field
2004
P. Levitt
1,854
44.14
37.59
perspective on society
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Rethinking assimilation theory for a new era of immigration
1997
R. Alba
10
Conceiving and researching transnationalism
1999
S. Vertovec
1,791
1,510
42.64
24.15
45.76
19.82
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late 1990s or 2000s. This nicely shows that an increase in empirical studies, the rise of the
concept of transnationalism, and the increasing institutionalization of migration studies at
the beginning of the 1990s (vgl. Vertovec, 2020) led to some important conceptual contribu-
tions at the end of the 1990s. The foundational works assembled in the table are not single
case studies but provide summarizing reviews or theoretical approaches to migration.
Applying our metric MigCite to put paper citations in the context of the first author’s field of
origin, the ranking among top papers changes because these top papers receive interdisciplin-
ary attention as migration studies papers and not as papers from their authors’ fields of origin.
Top papers received only a fraction of their citations from within the field of migration. Daher,
the total number of citations is not necessarily a reliable indicator for how much a paper has
shaped the migration studies field. This becomes obvious when applying our MigImp metric,
which would put the paper with seventh most overall citations on top of the list. Jedoch,
ranking all papers according to MigImp reveals that the most cited papers retain their top posi-
tions with only four less-frequently cited papers entering the top 10 list.
3.2.
Influential Journals, Authors, Institutions, and Countries
When examining the number of papers published as well as the number of citations in our
corpus, the most influential journals in migration studies tend to stand out. Zum Beispiel, A
rather high share of self-citations of Language, Culture and Curriculum might be also due to
the fact that it is a highly specialized topic. Concentration on the aspect of language stands in
contrast to other migration journals with a wider scope. Daher, referring to articles with this
special focus and thereby citing from the journals’ previous articles might be necessary. Der
same might apply for the second ranked journal Race, Ethnicity and Education, which focuses
on educational aspects, whereas for instance the Journal of Ethnic and Migration Studies as
well as Ethnic and Racial Studies (vgl. Tisch 1) do not have this limitation.
Quantitative Science Studies
1475
Mapping and impact assessment of phenomenon-oriented research fields
Tisch 4.
First authors’ institutions—authors with most publications
Rank
1
Name
M. Banton
Current institution
University of Bristol
2
3
4
5
M. Verkuyten
European Research Centre on
Migration & Ethnic Relations
D. Massey
Princeton University
B. S. A. Yeoh
National University
of Singapore
G. Hugo
The University of Adelaide
Total
articles
45
42
39
36
32
Total
citations
440
1,224
4,937
1,616
Avg. citations
per article
9.78
Avg.
MigCite
0.23
MigImp
15.1
29.14
0.86
8.98
126.59
44.89
3.01
1.00
32.6
29.4
1,767
55.22
2.91
11.3
To identify the most influential people in the field, we studied productivity in terms of not
only papers that an author contributes to (vgl. Tisch 4) but also the number of citations that an
author’s papers receive (vgl. Tisch 5). Gesamt, these two aspects are moderately correlated
(rPearson = 0.54), indicating that writing more articles strengthens one’s overall recognition in
the field of migration. Gleichzeitig, publishing a groundbreaking work that receives many
individual citations also greatly helps to boost an author’s recognition and overall citation
zählen, as the most cited authors’ high level of average citations shows. Consider for example
S. Vertovec, who wrote the fifth most influential paper in terms of MigImp (vgl. Tisch 3) Und
received the highest number of overall citations in the field. This feature may be especially
salient in migration studies because the field is relatively young and because some founda-
tional texts and authors are well recognized among researchers of different disciplinary back-
grounds. Some of the most distinguished persons in the field also act(Hrsg) as editors of migration
journals (z.B., S. Schwartz and S. Vertovec). Vor allem, several highly cited researchers have
already passed away or are emeriti. This correlates to the list of highly cited papers of which
several were published in the 1990s. Tisch 4 further shows that, Zum Beispiel, Graeme Hugo
has more total and average citations, offering a broad portfolio of articles that are widely cited
(vgl. also Table 5), but that Brenda Yeoh outperforms him when it comes to the impact within
the migration research community.
When moving from author to institution and to country level, we always attribute a paper to
the first author’s (current) institution. While this approach helps to avoid the disproportionate
impact of multiauthored papers (especially for intrainstitutional collaborations) on institution
Tisch 5.
First authors’ institutions—authors with most citations
Rank
1
Name
S. Vertovec
Current institution
Max Planck Institute for the Study
of Religious and Ethnic Diversity
2
3
4
5
C. Station
CQUniversity Noosa
A. Portes
University of Miami
D. Massey
Princeton University
R. Alba
The Doctorate-Granting Institution of
the City University of New York
Total
citations
7,266
Total
articles
17
Avg. citations
per article
427.41
Avg.
MigCite
12.95
MigImp
80.8
5,841
5,161
4,937
4,215
22
20
39
23
265.5
258.05
126.59
183.26
3.64
45.1
10.32
114.0
3.01
4.36
32.6
63.0
Quantitative Science Studies
1476
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Tisch 6.
rank 6)
Rank
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2
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4
5
6
Top institutions in terms of number of publications. ( We opted to show the top six cases here because there was a larger gap after
Institution
Universität Oxford
Universität von Kalifornien, Los Angeles
Universität von Toronto
Universiteit van Amsterdam
University of Sussex
National University of Singapore
Total
articles
316
254
201
177
170
148
Total
citations
16,854
13,619
7,340
6,184
7,188
6,436
Avg. authors
per article
1.5
Avg. citations
per article
53.3
2.13
1.59
1.56
1.4
1.71
53.6
36.5
34.9
42.3
43.5
impact, it may neglect crucial contributions of non-first authors. Bedauerlicherweise, we could not
retrieve data on other authors due to database access limitations (vgl. Abschnitt 4.2).
Es gibt 4,074 unique institutions out of which 2,249 (55.2%) published only once. An
average, an institution published 5.97 migration studies papers in our observation period with
a median of 1. This distribution indicates that most institutions only publish occasionally on
migration topics rather than having dedicated migration studies research groups. Jedoch,
there are some highly productive institutions in terms of migration papers published (listed
in Table 6). All of these institutions are universities, indicating that most migration studies
research is academic. The University of Oxford ranks first with regard to the number of articles
as well the as number of citations. It is one of the few universities that offer an MSc and a DPhil
in Migration Studies. Weiter, this program was designed as a cooperation between two depart-
gen (School of Anthropology and Oxford Department of International Development) welche
itself shows that migration research connects different disciplines and often is a joint effort.
Rank 2, the UCLA Center for the Study of International Migration, is fostered by several fac-
ulties as well and it offers a minor in International Migration Studies. The minor programs of
Critical Migration Studies at the University of Toronto and Global Migration at the University
of Amsterdam show that there are academic staff (and not only single researchers) interested in
the field. Jedoch, when investigating the average citation count of papers published by an
institution (vgl. Tisch 7), one finds a mention of the World Bank, which most likely supplies
statistical figures from its migration data portal to which researchers refer. The same likely
Tisch 7.
included institutions with more than 10 papers published)
Average citations of papers main-authored by institution member. (To reduce distortion by a single well-received paper, we only
Rank
1
2
3
4
5
6
Institution
University of New Hampshire Durham
University of Canterbury
University of Greenwich
Max Planck Institute for Demographic Research
The World Bank, USA
National University of Singapore
Quantitative Science Studies
Total
articles
12
Total
citations
2,680
Avg. authors
per article
1.83
Avg. citations
per article
223
15
18
13
27
148
2,874
2,651
1,499
3,010
6,436
2.4
2.39
2.46
2.22
1.71
192
147
115
111
43
1477
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Mapping and impact assessment of phenomenon-oriented research fields
holds for the Max Planck Institute for Demographic Research. Interessant, zum Beispiel, In
the study by Sweileh et al. (2018) the UN and WHO presented themselves as prominent
actors, zu.
Aggregating impact on a country level reveals a strong dominance of US and UK institu-
tionen, such as the University of New Hampshire Durham, University of Greenwich, Boston
College, University at Albany, and Duke University, whose members first-authored almost half
of the articles in our corpus. Gesamt, we observe a strong dominance of countries of the
Global North also with regard to number of articles, as Figure 4 shows—dominated by the
United States with 8,711 and United Kingdom with 4,389. Both countries are also citing
articles from each other, wie in der Abbildung ersichtlich 5. The figure shows the countries’ citation net-
arbeiten (d.h., citations from papers in one country’s institution to papers from another country’s
institution). We show only those countries having more citations than the average citations
among countries. The size of arrows represents the fraction of citations from another country
(vgl. Figur 5).
Researchers from 147 different countries participated in the production of the retrieved
base articles. The Global North’s dominance in the field suggests an underrepresentation
of scholars from the Global South. This could be a manifestation of what Strukelj (2020,
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Figur 4. Number of publications on country level.
Quantitative Science Studies
1478
Mapping and impact assessment of phenomenon-oriented research fields
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Figur 5. Countries’ citation network: most relevant collaborations between authors’ countries of
origin.
P. 177) calls the “receiving-country bias,” meaning that Western scholars prevail in taking
the perspective of immigration rather than emigration (Das, zu, is supported by the results of
Sweileh et al. (2018)).
We were able to identify countries that did not generate much publication output but did
generate quality output (in terms of citations) by calculating the average citations per article for
each country. We considered countries with at least 100 publications in the corpus (vgl.
Tisch 8). There are two articles published by authors from Singapore in the year 1999. Eins
of the articles (Station & Kennedy, 1999) received more than 700 citations. Ähnlich, for New
Zealand, out of four articles published in the year 1990, one article (Searle & Station, 1990) got
1,220 citations, which makes New Zealand a high-level citation country.
3.3. Collaboration Network
In diesem Abschnitt, we shed light on how often and with whom the authors collaborated. Das ist ein
sign of how interconnected the field is. We assume that there will be fewer collaborations than
in other (disciplinarily closed) fields. Daher, this measurement can also be an indicator for other
phenomenon-oriented research areas. Zusätzlich, we find that the number of authors per arti-
cle rises over time. On average, es gibt 1.77 authors per paper and 42% of the articles are
the product of collaboration between multiple authors. Somit, wenngleich 58% of the papers
were written by single authors, studying collaboration networks seems worthwhile. The whole
collaboration network of our data includes 35,102 authors and 41,925 collaboration relations
(weighted by frequency of collaboration of two authors) between them, Aber 27% (n = 9,196) von
the authors never collaborated at all, and overall only 6% of authors collaborated more than
once. The overall network density of collaborating authors is remarkably low at 0.012%, sogar
compared to other networks of interdisciplinary research fields (z.B., 0.34% for electronic mar-
ket research (Fischbach, Putzke, & Schoder, 2011)).
Quantitative Science Studies
1479
Mapping and impact assessment of phenomenon-oriented research fields
Tisch 8. List of countries by average citations per article
Country
Singapur
Neuseeland
Vereinigte Staaten
Österreich
Kanada
Großbritannien
Belgien
Norwegen
Niederlande
Australia
Number of articles
202
230
8,711
141
1,499
4,389
299
251
887
1,489
Total citations
7,940
8,971
305,439
4,441
4,719
13,062
8,657
7,235
25,445
41,742
Average number of authors
1.62
Average citations per article
39.31
1.89
1.96
1.85
1.80
1.54
2.17
1.79
1.98
1.81
39.00
35.06
31.50
31.49
29.76
28.95
28.82
28.69
28.03
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munities containing at least 10 authors, 43 of which contain at least 20 authors. Thereby, Die
giant component (vgl. Figur 6) Merkmale 5,491 authors and 12,563 collaboration relations.
Within these communities, collaboration is ubiquitous as demonstrated by the high clustering
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Figur 6. Giant component of the collaboration network. (Featuring 5,491 authors and 12,563 collaboration relations; colors highlight closer
connected subclusters of different fields).
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Mapping and impact assessment of phenomenon-oriented research fields
coefficient of 0.71. Migration as a diverse phenomenon prompts research from various per-
spectives that are still bound to their disciplines. Unlike, Zum Beispiel, advertising that is an
interesting field based on somewhat “closer” disciplines, such as psychology, economics,
and marketing, the scope of migration is much wider and binds the interest of disperse fields
such as geography and communication studies that might only very seldom work in interdis-
ciplinary settings but prefer close disciplinary ties. Somit, the clustering is considerably higher
than in the larger, more general fields of Biology (0.066), Mathematik (0.15), or Physics (0.43)
(Newman, 2004) and on par with more specialized disciplines such as Statistics (.611),
Nanoscience (.776) or Pharmacology (.775) (González-Alcaide, Pinargote, & Ramos, 2020).
Im Gegensatz, the monothematic interdisciplinary field of Electronic Market Research exhibits
an even higher degree of clustering (.875) (Fischbach et al., 2011).
Trotzdem, the case of migration studies also demonstrates the value of interdisciplinary
collaboration featured in the giant component of the collaboration network (vgl. Figur 6) Das
consists of 15.64% of authors. Three of the five authors with the highest publication count
obtain central positions in the network. Zum Beispiel, M. Verkuyten works at an interdisciplin-
ary research center which even bridges subcommunities of different fields. Andererseits,
M. Banton, the most productive author, is not part of this interdisciplinary network. Darüber hinaus,
four of the five most cited authors are also not frequent collaborators. Somit, homophily and
intensive collaboration, especially with one’s students (indicated by a high eigenvector cen-
trality (Xu & Chang, 2020)) or repeatedly with similar authors (Abbasi, Altmann, & Hwang,
2010) but also working in isolation seem viable strategies in a multidisciplinary field such
as migration studies. To date, it is not yet possible to judge the long-term success of the most
frequent collaborator S. Schwartz (83 collaborations) because his works were published only
nach 2015.
4. DISKUSSION
4.1. Assessing Research Impact of Phenomenon-Oriented Research Fields
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Since the emergence of scientometry, the focus has been on the big disciplines trying to look
for a closed set of actors that make up a distinct discipline and identify its leading researchers
(z.B., Milman and Gavrilova (1993) for chemical engineering). This is why we consider it
important not to stick to the respective discipline but to look for research impact assessment
within an interdisciplinary phenomenon-oriented research field. In diesem Artikel, we proposed a
novel framework for assessing the research impact of phenomenon-oriented research by con-
sidering the example of migration studies, which currently is at the heart of political debates.
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Our analysis employed SKGs to unravel the citation and collaboration structure of the
migration studies research field. Regarding collaboration, we find a high level of clustering
that indicates ubiquitous collaboration despite the majority of papers being written by single
authors. Specifically in migration studies, leading interdisciplinary cooperation projects
seem especially beneficial to one’s citation success. Jedoch, collaborations are based not
only on the proximity of research topics but often on the thematic and geographical proximity
of research institutions.
Comparison of research impact across disciplines is often difficult due to discipline-specific
collaboration and citation patterns. This specifically affects interdisciplinary research areas
and obscures the assessment of the most influential works, authors, or journals in that field.
To counteract this, as it relates to citations, we proposed the MigCite-Factor and MigImp metric
that discount research impact according to the field that the paper, author, or journal come
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aus. Darüber hinaus, the MigCite-Factor identifies the most important papers of the field itself by
taking both general visibility and within-field citations into account. One can straightforwardly
apply this approach to other interdisciplinary fields. Zum Beispiel, the field of terrorism
research fulfills many of the characteristics that we have identified for migration research: Es
is inter- and multidisciplinary, is relatively young, and does not have a coherent fund of
definitions, theories, concepts, and methods. Even though there have been first attempts to
apply social network analysis to its research objects, (d.h., terrorist groups; Perliger & Pedahzur,
2011), the researchers’ networks have not been the focus of analysis. Daher, it would be inter-
esting to apply our new research impact assessments and citation procedures to terrorism
Forschung.
4.2. Strengths and Limitations
Our research was motivated by the question of how we can reliably measure scientific col-
laboration, productivity, Qualität, and impact, if specific field-related structures influence the
outcome and extent of scientific or even practical impact (z.B., applications in medical pro-
cedures or in migration policy institutions). Our findings support what Fortunato et al. (2018)
asserted: “Science often behaves like an economic system with a one-dimensional ‘currency’
of citation counts. […] Science can be improved by broadening the number and range of per-
formance indicators.” We propose at least weighting the classical impact metrics by the disci-
plinary background of the paper, author, or journal that they evaluate to allow for meaningful
comparison in an interdisciplinary field such as migration studies. Working with such weighted
measures may also inform the decisions of appointment committees or funding bodies.
Despite our comparatively large corpus, our analysis may still miss important clusters of the
migration research debate, as we started from a fixed list of journals and retrieved only those
additional articles that were somehow connected to works published in those journals. Wir
aimed for the golden mean of including as many relevant works as possible while avoiding
irrelevant ones, but one may weight these two goals differently than we did.
Außerdem, the Scopus database is limited: Non-indexed journals or gray literature did
not contribute to our sample. Endlich, the validity of our corpus depends on the validity of
Pisarevskaya et al.’s (2020) list of journals, as we used this list as a baseline for our own
sampling. We are well aware that a change in our sampling strategy could have affected our
results; nevertheless, we believe them to draw a valid picture of the field of migration research.
Beside these specific issues, we encountered several problems posed by the limitations of
bibliometric databases. Erste, different spellings of authors’ and institutions’ names, or multiple
authors with identically spelled names, authors that sometimes publish with or without
including their second name, and name changes after marriage might have negatively
impacted data integrity despite our cleaning efforts. This is what initiatives such as ORCID
or the Web of Science ResearcherID try to overcome. Außerdem, access to authors’ fields
of origin via a database would allow for comparing our MigCite-Factor with the traditional cita-
tion count not only for the most cited papers and most important authors but for the whole data
set. Zweite, the methodological implementation of our scientometric study is somehow lim-
ited in the sense that the delineation of the field of migration research is performed based on
the journal list developed by Pisarevskaya et al. (2020). Daher, we condone that it incorporates
any limitations of the delineation performed in Pisarevskaya et al. (2020). Weiter, we could
only retrieve the first author of a paper using the Scopus API. As a workaround, we extracted
the other authors via the Semantic Scholar API, which does not provide affiliation data.
Dritte, the scattering of our data across the two databases is also a major obstacle that
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may lead to failed retrieval of some data sets (missing influential papers in our SKGs as a worst-
Fall). This obstacle is especially exigent for interdisciplinary, phenomenon-oriented research
Daten, as papers are scattered over different journals and appear in different disciplinary back-
grounds. In this case, it is of no use to look for simple citation counts that quantify the impact of
a paper regardless of its disciplinary or topic-related environment.
4.3. Conclusion and Outlook
At present, research data are more accessible and available without cost than ever before
Und, due to the massive digitalization of older documents, research from past decades is made
readily available. Zusätzlich, there is a growing interest throughout all research communities
in retrieving reliable data that allows the individual researcher to rank their work and impact
in comparison within the community—a community that is shifting depending on the subject
of the research. We assumed that, as inter- and multidisciplinary research fields do not have
their own databases and because the number of papers as well as the citations and impact
factors vary substantially across research fields, a tailored, context-sensitive representation
of the impact of knowledge is needed for these fields. In our opinion, the proposed metric
has the potential to provide such a representation. With our paper, we hope to contribute
to the efforts of creating synergies between hitherto divided research communities. Our anal-
ysis leads to several suggestions for future research: First of all, when assessing impact of
papers or authors, we do not weight citations other than by discriminating between citations
from within and outside migration studies for MigImp. Somit, putting higher weight on cita-
tions from more important papers (Chen et al., 2007) seems a logical next step. Applying our
approach to another interdisciplinary field such as terrorism research would not only aid in its
further validation but also help in understanding both fields in greater detail.
Even though we assume that the field of migration research is male-dominated, we did not
conduct an analysis of gender inequalities in authorship because the metadata did not include
authors’ gender. Zusätzlich, given the limited availability of full names, we were unable to
identify gender and could also not resort to face recognition, Zum Beispiel. Regarding homo-
phily, it would be interesting to see the characteristics of authors that lead to forming the
mutual bonds of collaboration. Perhaps it is dependent upon the sociocultural background
(US and UK institutions) or related to institutional standing (similar levels of hierarchy between
collaborating researchers), or is it that collaborations between male vs. female researchers pre-
vail or is it a question of age?
Another area for further exploration would be to couple our analysis of authors with a topic
modeling of their research. This could answer questions such as: How far do specific topics
influence research activities? Do we find clusters of researchers around specific subtopics?
And how are these connected? Can we identify trending subtopics that always rank high in
citations and would be profitable career-wise? It is beyond the scope of this paper to give
answers to questions such as these. Noch, we hope to have given a starting point and to have
initiated further debate about how to measure research impact in phenomenon-oriented
research fields.
ACKNOWLEDGMENT
We developed the initial idea for this project at the Summer Institute in Computational Social
Wissenschaft (SICSS) in Bamberg 2019 (https://sicss.io/2019/bamberg/). Thanks to all for the input
we received at the Institute. Thank you also to Lucas Sage for commenting on a draft version of
Quantitative Science Studies
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this paper. Another “Thank you” goes to the reviewers who have given us very constructive
and detailed feedback.
BEITRÄGE DES AUTORS
Liane Rothenberger: Konzeptualisierung, Formale Analyse, Untersuchung, Methodik, Valida-
tion, Writing—original draft, Writing—review & Bearbeitung. Muhammad Qasim Pasta: Conceptu-
alization, Datenkuration, Formale Analyse, Untersuchung, Methodik, Software, Validierung,
Visualisierung, Writing—original draft, Writing—review & Bearbeitung. Daniel Mayerhoffer: Con-
ceptualization, Formale Analyse, Untersuchung, Methodik, Validierung, Writing—original
Entwurf, Writing—review & Bearbeitung.
COMPETING INTERESTS
The authors have no competing interests.
FUNDING INFORMATION
No funding has been received by Rothenberger or Pasta. Mayerhoffer has received funding
from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, grant num-
ber 430621735), a self-governed research funding organization, funded by the German public
purse that did not influence the content of this paper in any way. This work was supported by
the German Research Foundation (DFG) within the funding program Open Access Publishing.
DATA AVAILABILITY
Processed data, high-resolution figures, tables and supplementary material along with guide-
lines for open reusage are available at the following Zenodo repository: https://doi.org/10
.5281/zenodo.5113133.
VERWEISE
Abbasi, A., Altmann, J., & Hwang, J. (2010). Evaluating scholars
based on their academic collaboration activities: Two indices,
the RC-index and the CC-index, for quantifying collaboration
activities of researchers and scientific communities. Scientomet-
rics, 83(1), 1–13. https://doi.org/10.1007/s11192-009-0139-2
Castles, S., & Müller, M. J. (1993). The age of migration: Interna-
tional population movements in the modern world. Macmillan.
Chen, P., Xie, H., Maslov, S., & Redner, S. (2007). Finding scientific
gems with Google’s PageRank algorithm. Journal of Informetrics,
1(1), 8-15. https://doi.org/10.1016/j.joi.2006.06.001
Fischbach, K., Putzke, J., & Schoder, D. (2011). Co-authorship net-
works in electronic markets research. Electronic Markets, 21(1),
19–40. https://doi.org/10.1007/s12525-011-0051-5
Fortunato, S., Bergstrom, C. T., Börner, K., Evans, J. A., Helbing, D.,
… Barabási, A.-L. (2018). Science of science. Wissenschaft, 359(6379).
https://doi.org/10.1126/science.aao0185, PubMed: 29496846
González-Alcaide, G., Pinargote, H., & Ramos, J. M. (2020). Aus
cut-points to key players in co-authorship networks: A case study
in ventilator-associated pneumonia research. Scientometrics,
123(2), 707–733. https://doi.org/10.1007/s11192-020-03404-w,
PubMed: 32431466
Granovetter, M. S. (1973). The strength of weak ties. amerikanisch
Journal of Sociology, 78(6), 1360–1380. https://doi.org/10.1086
/225469
Hassan, S.-U., Visvizi, A., & Waheed, H. (2019). The “who” and the
“what” in international migration research: Data-driven analysis
of Scopus-indexed scientific literature. Behaviour & Information
Technologie, 38(9), 924–939. https://doi.org/10.1080/0144929X
.2019.1583282
Hays, G. C., & Scott, R. (2013). Global patterns for upper ceilings
on migration distance in sea turtles and comparisons with fish,
birds and mammals. Functional Ecology, 27(3), 748–756.
https://doi.org/10.1111/1365-2435.12073
Kajikawa, Y., Ohno, J., Takeda, Y., Matsushima, K., & Komiyama,
H. (2007). Creating an academic landscape of sustainability sci-
enz: An analysis of the citation network. Sustainability Science,
2(2), 221–231. https://doi.org/10.1007/s11625-007-0027-8
Marres, N., & de Rijcke, S. (2020). From indicators to indicating
interdisciplinarity: A participatory mapping methodology for
research communities in-the-making. Quantitative Science Stud-
ies, 1(3), 1041–1055. https://doi.org/10.1162/qss_a_00062
Marshakova, ICH. V. (1981). Citation networks in information science.
Scientometrics, 3(1), 13–25. https://doi.org/10.1007/bf02021861
Milman, B. L., & Gavrilova, Y. A. (1993). Analysis of citation and
co-citation in chemical engineering. Scientometrics, 27(1), 53–74.
https://doi.org/10.1007/BF02017755
Moed, H. F. (2005). Citation Analysis in Research Evaluation.
Dordrecht: Springer. https://www.springer.com/gp/ book
/9781402037139
Newman, M. E. J. (2004). Coauthorship networks and patterns of
scientific collaboration. Proceedings of the National Academy
of Sciences of the United States of America, 101 Suppl 1,
5200–5205. https://doi.org/10.1073/pnas.0307545100,
PubMed: 14745042
Quantitative Science Studies
1484
l
D
Ö
w
N
Ö
A
D
e
D
F
R
Ö
M
H
T
T
P
:
/
/
D
ich
R
e
C
T
.
M
ich
T
.
/
e
D
u
Q
S
S
/
A
R
T
ich
C
e
–
P
D
l
F
/
/
/
/
2
4
1
4
6
6
2
0
0
7
9
1
7
Q
S
S
_
A
_
0
0
1
6
3
P
D
.
/
F
B
j
G
u
e
S
T
T
Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3
Mapping and impact assessment of phenomenon-oriented research fields
Patience, G. S., Patience, C. A., Blais, B., & Bertrand, F. (2017).
Citation analysis of scientific categories. Heliyon, 3(5). https://
doi.org/10.1016/j.heliyon.2017.e00300, PubMed: 28560354
Perliger, A., Pedahzur, A. (2011). Social network analysis in the study
of terrorism and political violence. Political Science and Politics,
4(1), 45–50. https://doi.org/10.1017/S1049096510001848
Pisarevskaya, A., Erheben, N., Scholten, P., & Jansen, J. (2020). Map-
ping migration studies: An empirical analysis of the coming of
age of a research field. Migration Studies, 8(3), 455–481.
https://doi.org/10.1093/migration/mnz031
Popp, J., Schmied, S., Balogh, P., & Jámbor, A. (2016). Co-authorship
and co-citation networks in the agricultural economics literature:
The case of Central and Eastern Europe. Eastern European
Economics, 54(2), 153–170. https://doi.org/10.1080/00128775
.2015.1135065
Radicchi, F., Fortunato, S., & Castellano, C. (2008). Universality of
citation distributions: Toward an objective measure of scientific
impact. Proceedings of the National Academy of Sciences of the
USA, 105, 17268–17272. https://doi.org/10.1073/pnas
.0806977105, PubMed: 18978030
Radicchi, F., Fortunato, S., & Vespignani, A. (2012). Zitat
Netzwerke. In A. Scharnhorst, K. Börner, & P. van den Besselaar
(Hrsg.), Understanding complex systems. Models of science
Dynamik (S. 233–257). Springer. https://doi.org/10.1007/978
-3-642-23068-4_7
Redner, S. (2005). Citation statistics from 110 years of Physical Review.
Physics Today, 58(6), 49–54. https://doi.org/10.1063/1.1996475
Schwarz, G. M., & Stensaker,
(2016). Showcasing
phenomenon-driven research on organizational change. Zeitschrift
of Change Management, 16(4), 245–264. https://doi.org/10.1080
/14697017.2016.1230931
ICH. G.
Searle, W., & Station, C. (1990). The prediction of psychological and
sociocultural adjustment during cross-cultural transitions. Inter-
national Journal of Intercultural Relations, 14(4), 449–464.
https://doi.org/10.1016/0147-1767(90)90030-Z
Small, H. (1973). Co-citation in the scientific literature: A new mea-
sure of the relationship between two documents. Journal of the
American Society for Information Science, 24(4), 265–269.
https://doi.org/10.1002/asi.4630240406
Strukelj, T. (2020). Reflections on methods and methodology in
migration studies. In A. Schulz & T. Schwertel (Hrsg.), Der lange
Sommer der Flucht – 2015 und die Jahre danach (S. 173–184).
Barbara Budrich. https://doi.org/10.2307/j.ctv153k59d.10
Sweileh, W. M., Wickramage, K., Pottie, K., Hui, C., Roberts, B., …
Zyoud, S. H. (2018). Bibliometric analysis of global migration
health research in peer-reviewed literature (2000–2016). BMC
Public Health, 18(1), 777. https://doi.org/10.1186/s12889-018
-5689-X, PubMed: 29925353
Van Eck, N. J., & Waltman L. (2010). Software Survey: VOSviewer,
a Computer Program for Bibliometric Mapping. Scientometrics,
84(2), 523–38. https://doi.org/10.1007/s11192-009-0146-3,
PubMed: 20585380
Vertovec, S. (2020). Two cheers for Migration Studies. Comparative
Migration Studies, 8(38). https://doi.org/10.1186/s40878-020
-00195-0
Wang, Q., & Schneider, J. W. (2020). Consistency and validity of
interdisciplinarity measures. Quantitative Science Studies, 1(1),
239–263. https://doi.org/10.1162/qss_a_00011
Station, C., & Kennedy, A. (1999). The measurement of sociocultural
adaptation. International Journal of Intercultural Relations, 23(4),
659–677. https://doi.org/10.1016/S0147-1767(99)00014-0
Xu, Q. A., & Chang, V. (2020). Co-authorship network and the
correlation with academic performance. Internet of Things, 12,
100307. https://doi.org/10.1016/j.iot.2020.100307
Yan, E., & Ding, Y. (2011). Discovering author impact: A PageRank
Information Processing & Management, 47(1),
Perspektive.
125–134. https://doi.org/10.1016/j.ipm.2010.05.002
Zlotnik, H. (1998). International migration 1965–96: An overview.
Population and Development Review, 24(3), 529–468. https://doi
.org/10.2307/2808151
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