RESEARCH ARTICLE

Field-level differences in paper and author
characteristics across all fields of science
in Web of Science, 2000–2020

a n o p e n a c c e s s

j o u r n a l

Danish Centre for Studies in Research and Research Policy, Aarhus University, Aarhus, Denmark

Jens Peter Andersen

Keywords: field definitions, publication analysis, publication characteristics, scientific
communication, scientific fields, scientific norms

ABSTRACT

With increasing availability of near-complete, structured bibliographical data, the past decade
has seen a rise in large-scale bibliometric studies attempting to find universal truths about
the scientific communication system. Tuttavia, in the search for universality, fundamental
differences in knowledge production modes and the consequences for bibliometric assessment
are sometimes overlooked. This article provides an overview of article and author characteristics
at the level of the OECD minor and major fields of science classifications. The analysis relies
on data from the full Web of Science in the period 2000–2020. The characteristics include
document type, median reference age, reference list length, database coverage, article length,
coauthorship, author sequence ordering, author gender, seniority, and productivity. The article
reports a descriptive overview of these characteristics combined with a principal component
analysis of the variance across fields. The results show that some clusters of fields allow inter-
field comparisons, and assumptions about the importance of author sequence ordering, while
other fields do not. The analysis shows that major OECD groups do not reflect bibliometrically
relevant field differences, and that a reclustering offers a better grouping.

1.

INTRODUCTION

The vastness of available bibliographic metadata has created a fertile field for bibliometric and
scientometric research, and related areas. These areas have recently grown, not just in paper
volume, diversity in methods, and measurements but also very much so in the amount and
availability of data used in often global analyses of publication data. This has created a par-
adox of sorts, with the opportunities of the data on one hand, and the limitations and com-
plexities of the same data on the other. Problems that are solvable in small-scale analyses, come
as author name disambiguation or precise field delineation, become more difficult as the
quantity of data grows. Assumptions about universal comparability across data also become
harder as the magnitude and inclusivity of data increase.

Bibliometric research has often relied on an argument of random errors, which are assumed
to even out when the lens is shifted from the individual object (where one may find missing
and erroneous citations and references, or deliberate differences in the intentions of a refer-
ence (per esempio., critica)) to the statistical aggregate (Van Raan, 1998). This is a meaningful
approach, but it requires that the objects that are aggregated are more or less the same type
of objects. Naïvely, we could assume that scientific publications are all the same; Tuttavia, it is

Citation: Andersen, J. P. (2023). Field-
level differences in paper and author
characteristics across all fields of
science in Web of Science, 2000–2020.
Quantitative Science Studies, 4(2),
394–422. https://doi.org/10.1162/qss_a
_00246

DOI:
https://doi.org/10.1162/qss_a_00246

Peer Review:
https://www.webofscience.com/api
/gateway/wos/peer-review/10.1162
/qss_a_00246

Received: 7 novembre 2022
Accepted: 25 Gennaio 2023

Corresponding Author:
Jens Peter Andersen
jpa@ps.au.dk

Handling Editor:
Ludo Waltman

Copyright: © 2023 Jens Peter
Andersen. Published under a Creative
Commons Attribution 4.0 Internazionale
(CC BY 4.0) licenza.

The MIT Press

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Field-level differences in paper and author characteristics

well established that there are rather large differences in, Per esempio, referencing practices
between fields (Glänzel & Schubert, 2003; Leydesdorff & Bornmann, 2011). This makes
direct comparisons of citation counts and averages between fields somewhat meaningless.
One might even argue that unstable changes even make comparisons within fields some-
what meaningless over longer time spans, whether those changes are due to changes in
database inclusion or an absolute change in publication intensity (Nielsen & Andersen,
2021; Petersen, Pan et al., 2019). This is not new, and sophisticated indicators have been
developed to account for some of these field differences in citation impact (Waltman & van
Eck, 2019).

Tuttavia, variation in citation intensity is not the only difference between fields of
research. Fundamentally, there is a difference between the objects studied, which is linked
to the ontological and epistemological assumptions of a field. This influences the norms and
modes of knowledge production and authorship, along with varying norms in referencing and
credit distribution, as well as expectations for outputs and impacts. We know a lot about
bibliometrically relevant field differences already, such as about longer citing half-lives in
certain fields (Glänzel & Schoepflin, 1999; Zhang & Glänzel, 2017), large collaborations in
others (Piro, Aksnes, & Rørstad, 2013), latent rules about authorship positions (per esempio., Burrows &
Moore, 2011), and differences in preferred publishing venues and publication types (Bourke
& Butler, 1996; Kulczycki, Engels, & Nowotniak, 2017; Sigogneau, 2000). We also know a
great deal about the people behind the research, such as their career paths and how factors
such as gender play a large role in the careers of scientists (per esempio., Jagsi, Guancial et al., 2006;
Lerchenmüller, Lerchenmueller, & Sorenson, 2018; Xie & Shauman, 1998). Tuttavia, most of
our knowledge about these field characteristics focuses on one or few particular aspects,
and/or on specific fields. This previous research has established our knowledge about these
field differences, and the narrowness of focus has been necessary, but we are lacking a broad
overview allowing field comparisons on the global scale, with a wide range of characteristics
across the board.

This article provides a comparative overview of field differences, as they can be mea-
sured from publications, as well as an analysis of field groupings based on these character-
istics. It provides both paper and author characteristics at the OECD fields of science minor
level (OECD, 2007), which corresponds rather well with traditional university departments.
Researcher-level (cioè., career or personal) characteristics are not included in this article.
Complete publication data from Web of Science (WoS), covering the period 2000 A
2020, are used to provide a primarily descriptive analysis. We also address the question
of whether common field delineations, and especially groupings on the major level, come
as the OECD fields of science are meaningful for bibliometric research.

As a final caveat regarding the analytical units of this article: While many field differences
are found on the individual level (cioè., that of the individual researcher), this is truly the core
of much new research on the sociology and science of science, and beyond the scope of
Questo articolo. Tuttavia, by documenting differences on the paper and author levels (cioè., IL
abstract metadata “authors” of articles, not the physical person behind this), I hope to
provide information for additional research on all three of these levels of research. It should
also be noted that this article will focus solely on journal articles, which is the predominant
publication form in most fields of science. Additional information about other publication
types will be included where relevant. This is detailed further in Section 3. It should also
be noted that several of the variables mentioned in the following are commonly employed
in bibliometric research, although they may not have all been studied as a property of a
scientific field.

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Field-level differences in paper and author characteristics

2. LITERATURE

Bibliometric comparisons between fields have always been problematic. It is long estab-
lished that the “expected” average citations to an article vary greatly from field to field,
and absolute numerical comparisons are not possible (per esempio., Leydesdorff & Bornmann,
2011). This has been addressed in different ways, either by calculating normalized scores
relative to the mean (per esempio., Lundberg, 2007; Waltman, Calero-Medina et al., 2012), or as
percentile-based indicators (Pudovkin & Garfield, 2009). While these approaches also have
limitations (per esempio., they depend somewhat on the size and activity as well as proper delinea-
tion of fields), they certainly increase comparability. But this is only the case for direct com-
parisons between large aggregates of publications (per esempio., on the university level).

When it comes to sociological analyses of science, or research on the many other facets of
science than citations, other field differences also matter. As an example, many analyses of
academic careers rely on the position of authors in the byline to infer seniority (Jian & Xiaoli,
2013; Milojević, Radicchi, & Walsh, 2018; Perneger, Poncet et al., 2017), Tuttavia, in some
fields the order of the byline is commonly alphabetical (Henriksen, 2019; Mongeon, Smith
et al., 2017; Waltman, 2012). Also, in terms of productivity, the expected output of a
researcher varies a lot from field to field, depending on which types of publications are pro-
duced, how many coauthors work together on creating a publication, and how long such a
publication is. For such types of analyses to make sense, we need to select meaningful fields to
limit analyses on and resist the temptation of using universal publication data sets, simply
because they are available. In the following, I will list those characteristics at the article and
author level that have been found to influence such field differences, or which have obvious
consequences for field selection. These will also be the characteristics that are included in the
current analysis. In addition to obvious characteristics, I also include the (inferred) gender of
authors as a variable. While there are many well-documented gender differences in academia
(per esempio., Allison & Stewart, 1974; Lerchenmüller et al., 2018; Xie & Shauman, 1998), it is perhaps
less obvious why this should be a field characteristic, as the gender distribution is probably not
causally related to, Per esempio, knowledge production modes, publication types, and refer-
ence list lengths. Tuttavia, it matters greatly for field selection and expectations around gen-
der differences for quantitative studies in the sociology of science.

In the final part of this article, field characteristics on both the article and author level are
used to explore a regrouping of fields through a principal component analysis of the included
variables. In this analysis, gender will not be included, based on the same reasoning as above.

2.1. Article Characteristics

2.1.1. Document types

Citation analysis, especially focused on the natural and health sciences, is often restricted to
particular publication types, namely original research articles in journals, recensioni, and some-
times letters, while omitting editorial material, comments, books, book chapters, reports, E
many other publication types. This is often a meaningful choice, as these are the document
types that are most commonly—in those sciences—part of the scientific communication
system. Tuttavia, in parts of the social sciences and the humanities, monographs, edited
volumes, art reviews, and a plethora of other document types are important. With different
document types comes variation in the length and work put into them, and the interpretation
in terms of productivity, the use and meaning of citations, collaboration forms, and in general
just the role they play in producing and communicating knowledge. Bourke and Butler (1996)
documented such field norms, showing the differences in which publication types were

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common in the sciences, social sciences, and humanities (broadly categorized). This is not a
static image though, as changes in norms have occurred over time, such as in the natural sci-
enze, where changing trends in the use of WoS publication type registrations were seen in
physics in the 1990s (Sigogneau, 2000). Even larger changes have recently been observed in
the social sciences and humanities, with substantial national differences in the distribution of
types as well as over time (Kulczycki et al., 2017; Kulczycki, Engels et al., 2018). Similar broad
differences were also observed by Piro et al. (2013).

2.1.2. Cited half-life

The cited half-life is the median age of an article’s references (cioè., the number of years between
when the article was published and the cited reference was published). This gives us an indi-
cation about the speed of research in a field (cioè., the degree to which knowledge is generated in
a cumulative mode). This indicator has been included in the Journal Citation Reports for many
years. Many studies include this indicator in some shape, and Leydesdorff (2009) argues that it
adds an additional dimension to traditional citation-based indicators. The majority of articles
using the indicator operationalize it rather than studying field differences. Important exceptions
to this are the studies by Glänzel and Schoepflin (1999) and more recently Zhang and Glänzel
(2017). In Glänzel and Schoepflin (1999), selected fields from the natural and social sciences
are compared, using mean reference age as one measure, highly similar to cited half-life. IL
same measure is used in Zhang and Glänzel (2017), and supplemented by the median reference
age, comparing changes in this measure across a number of fields from 1992 A 2014. They
show differences across fields of more than a factor of two, as well as some increase in mean
reference age over time, mostly due to an increased referencing rate of very old papers and
decreased referencing rate of very new papers.

2.1.3. Number of references and reference coverage

In addition to the cited half-life, the quantity of references in a field is one of the determining
factors in how many citations a given publication can be expected to receive. Differences
between fields in the reference list length is thus a central norm to consider when selecting
and especially comparing fields. This was a central part of the arguments of both Glänzel,
Schubert et al. (2011) and Leydesdorff and Bornmann (2011) for how to perform field delin-
eations when field-normalizing citation-based indicators. But not only are there differences in
the length of reference lists, the proportion of references covered by WoS also varies between
fields (per esempio., Kulczycki et al., 2018), which is both a question of document type distributions
(because journals are covered better than books and conference proceedings in WoS) E
field-specific journal coverage (Kulczycki et al., 2018).

2.1.4. Article length

Article length is one of the variables that have been studied as a driver for citations, for exam-
ple in management science (Stremersch, Verniers, & Verhoef, 2007) and accounting research
(Meyer, Waldkirch et al., 2018), with small, yet positive correlations. I would argue that while
there may be some theoretical sense behind this correlation (cioè., longer articles potentially
have additional content or are more thorough, and thus of higher quality), there is some spec-
ulation about referencing behavior in such an argumentation that does not align well with
known reasons for referencing scientific work (Garfield, 1996). Nonetheless, there seems to
be a correlation, and article length is also important for other reasons; both Stremersch
et al. (2007) and Meyer et al. (2018) looked at within-field article length variation, che è
a sensible delineation in both cases, but we should expect to see much greater between-field

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variation, as this is something that is expectedly tied to the disciplinary norms of a field (Fanelli
& Glänzel, 2013). While potential effects on expected citation outcomes between fields can be
solved through field normalization, it is less common to make such normalizations for the pro-
ductivity of researchers (cioè., the number of articles published by a scientist), and in this case an
assumption about how much work goes into the making of an article. Page count is far from a
perfect proxy for this, as a given page contains highly varying amounts of information (per esempio.,
tables, figures, and formulas in contrast to text quotations), and the assumption of work amount
is also highly field specific, COME, Per esempio, experimentation or field work does not directly
influence article length. Tuttavia, it is the only straightforward and available proxy allowing
us to analyze field differences in article length, and is included as a part of the puzzle of fig-
uring out which variables inform us about the bibliometrically relevant differences in knowl-
edge production between fields.

2.1.5. Coauthorship

Following naturally from the above, the work that goes into making an article also depends on
the number of collaborators, and it is well known that this is a norm with extreme variation
between fields. As an example, in Nielsen and Andersen (2021), physics and astronomy was
excluded from the analysis, as the number of coauthors (>1,000) on some articles skewed the
overall trends of all included fields. More systematically, Piro et al. (2013) studied productivity
and collaboration in terms of both whole counts and fractional counts across 37 fields in the
humanities, social sciences, natural sciences, medicine, and technology. Excluding the more
extreme cases (here >100 authors), there were still clear field differences, but with medicine
rising to be more collaborative than the natural sciences (which fell from 19.6 A 5.6 authors
per paper on average). Both Nielsen and Andersen (2021) and Fanelli and Larivière (2016)
have found that productivity per author has increased greatly when whole-counting publica-
zioni, but remained stable when taking into account the number of coauthors.

2.2. Author Characteristics

2.2.1.

Sequence ordering

It is obvious that the amount of work from any given author on an article with very long coau-
thor lists is different from that of articles from small groups or individual researchers. One of the
ways in which bibliometric research has approached this problem is by attributing greater
weight to the main author(S) of a publication. Tuttavia, this relies on an assumption that it
is possible to infer who the main author is. In some cases, this is the corresponding author;
Tuttavia, recent research has shown that the number of corresponding authors per paper has
increased over time and the position in the byline of the corresponding author(S) varies
between fields (Chinchilla-Rodríguez, Costas et al., 2022). Other research has shown that
intentional alphabetical ordering is highly field dependent, and generally decreasing over time
(Henriksen, 2019; Mongeon et al., 2017; Waltman, 2012), while in other fields there is a ten-
dency to assign special meaning to the first and last author position, namely that the first author
produced the majority of the work, wrote the article, and performed the experiments, and the
last author supervised, secured funding, or had similar leadership roles (Larivière, Desrochers
et al., 2016; Larivière, Pontille, & Sugimoto, 2021). In these studies, high agreement about the
role of first authors was found but less so on the last authors, which varied more in their role. In
Questo articolo, I will report both the probability of intentional alphabetical ordering and the
difference in academic age (seniority) and productivity of first and last authors to quantify
the tendency of a field to use equal distribution of credit (alphabetical ordering) and to assign
special value to the first and last author position.

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2.2.2.

Seniority

Seniority and academic age have been used in numerous studies as explanatory variables for
differences in, Per esempio, productivity and citedness. Milojević (2012) has shown connec-
tions between the time from first publication (academic age) and productivity, collaboration,
and the referencing behavior of scientists. Also the position in the network changes over time,
where more senior scientists tend to become more central in their collaboration network
(Wang, Yu et al., 2017).

2.2.3. Gender

Quantitative studies of gender differences in academia have existed at least since the 1970s
(Allison & Stewart, 1974; Cole, 1979; Cole & Zuckerman, 1984). Landmark studies since then
have shown, Per esempio, evidence of systemic differences in the productivity of men and
women, the differences in expectations for promotions, and the consequences for academic
careers (Xie & Shauman, 1998), and the very long lag of women in senior author positions
even after parity has been reached in the entry to scientific research in fields as large as clinical
medicine (Jagsi et al., 2006). The literature leaves no doubt about differences in both the pro-
ductivity and citedness between men and women in academia, although there are quite dif-
ferent results around the direction, magnitude, and underlying mechanisms of the difference
(per esempio., Andersen, Schneider et al., 2019; Caplar, Tacchella, & Birrer, 2017; Larivière, Vignola-
Gagné et al., 2011; Lerchenmüller et al., 2018; Nielsen, 2016, 2017; Pagel & Hudetz, 2011;
Thelwall, 2020). Some of these studies are also good examples of cases where author position
is used to assume additional importance of the first and sometimes last author (Andersen et al.,
2019; Jagsi et al., 2006; Lerchenmüller et al., 2018; Thelwall, 2020).

3. DATA AND METHODS

The analyses presented in this article are based on bibliographic data from the WoS, specifi-
cally through the in-house implementation at CWTS, Leiden University, which is a structured,
relational database implementation of WoS. WoS is one of the largest bibliographical data-
bases of scientific research, including citation indices. The major parts of the analyses are
limited to publications classified as journal articles published between 2000 E 2020 (n =
24,715,351), except for the analysis of document types, which uses all documents in WoS,
for the same period (n = 37,806,737). The analyses are based on data from the WoS citation
indices: Arts & Humanities Citation Index, Book Citation Index—Social Sciences & Humanities,
Book Citation Index—Science, Current Chemical Reactions, Emerging Sources Citation Index,
Index Chemiculus, Conference Proceedings Citation Index—Social Sciences & Humanities,
Conference Proceedings Citation Index—Science, Science Citation Index Expanded, and the
Social Sciences Citation Index. While some of these indices include very few journal articles,
they all provide reference data.

In addition to article metadata about the publication type, reference list, journal, publica-
tion year, page count, and author list, we also require an appropriate field classification and
disambiguated author sets, in order to know their academic age and productivity as well as
their inferred gender. These variables are considerably more difficult to collect than the meta-
dati, which is why I will describe the process thereof below.

3.1. Author Disambiguation

The process of correctly assigning all the publications a scientist has written to a set symbol-
izing that scientist’s oeuvre is difficult. Sometimes scientists will change their name, or use, for

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esempio, middle names interchangeably, and quite often more than one person has the same
name. Basing such an assignment purely on the author’s name will create multiple sets for
each name variant and group publications by different authors using the same name. More
advanced algorithms have been developed, using additional evidence, such as author affilia-
zioni, coauthor network, keywords of their articles, bibliographic coupling, and cocitations to
improve both the recall (the number of an author’s publications assigned to one set) and the
precision (the share of publications assigned to one set that are actually authored by this
author) of disambiguated author sets. Through the CWTS implementation of WoS, there is
access to the results of the author disambiguation algorithm by D’Angelo and van Eck
(2020), which reports 93.7% precision and 94.1% recall. This is higher than the earlier version
of the algorithm (Caron & van Eck, 2014), which was found to be the best performing disam-
biguation algorithm by Tekles and Bornmann (2020).

The errors that do arise in this disambiguation are almost entirely through creation of sin-
gletons (cioè., author sets with just one publication) due to a change in an author’s record (per esempio., UN
single article published with a different affiliation and with a different group of coauthors). In
addition to providing substantially more precise author sets than the raw WoS data would, Questo
approach also gives us data about the affiliation and first name of an author prior to 2008,
which is when WoS started systematically registering affiliations of authors.

3.2. Alphabetization

I use the alphabetization approach of Waltman (2012), where all papers with two or more
coauthors are first assigned a value, ai = 1 if their byline is alphabetically ordered and ai =
0 if not. As shorter bylines especially may be ordered alphabetically by chance, or as a result
of contribution-based ordering, Waltman (2012) calculates the probability of intentional alpha-
betical ordering. For this purpose, Waltman defines a set of N publications, where publication
Ni has ni authors. Inoltre, pi is the probability that a potential alphabetical ordering is
intentional, which is an estimate based on the number of authors. Waltman defines this
estimator, ^pi as

(cid:1)
ai − 1=
1 − 1
ð

(cid:3)
!
ni
!= Þ
ni

^pi

¼

and the overall probability of N using intentional alphabetical ordering, ^p, thus is

^p ¼

1

N

XN

i¼1

^pi

The ratio of papers with alphabetical ordering, (cid:2)UN , is simply

(cid:2)a ¼

1

N

XN

i¼1

ai

and can be observed directly from the bibliographic metadata. We expect that fields with high
degrees of intentional ordering will have distributions of ^p over time close to the distribution
Di (cid:2)UN; Tuttavia, for fields with norms for limited coauthorship, there may still be a difference
between the values, as intentionality is difficult to estimate in publications with few authors.

I use a strict ordering, based on surnames as they are reported, which could potentially
introduce some errors in cases where authors use, Per esempio, dual or compound surnames
such as “Van der Waal.” Obviously, this may diverge, in either direction, from the authors’

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intention; Tuttavia, as we are not interested in minor differences but broad field trends, questo è
an acceptable error.

3.3. Author Productivity

This article reports author productivity at the point of publication (cioè., how many other articles
each author in the byline has written when a given article is published). This is measured on a
per-publication basis as the number of publications by the author up until the year in which it
was published plus a share of the publications published in the same year, as exact publication
dates are not available for all publications. This fraction avoids counting the same article
multiple times while also allowing for the counting of publications from the same year. Noi
can express this productivity ρ of a given author in year y as

ρy ¼

Xy −1

i¼y0

Ni þ

1

Ny

where Ni is the number of articles coauthored by this author in year i. As we are looking at
within-field differences between first and last author, publication counts are not fractionalized.
There may be a potential bias here, as more senior scientists (with higher productivity) are
potentially more likely to join larger collaborations, regardless of field.

3.4. Gender Inference

When inferring the gender of an author, it is important to note that designations of “man” and
“woman” are probability-based estimates based on population statistics, and not necessarily
correct assumptions at the individual level. This is also why name-based gender inference
does not consider the biological sex of the person, or other genders than the two most com-
mon. The inferred genders used in this study are from the same data source as the Leiden
Ranking1, which uses a combination of genderize.io2 and gender-API3 to give the best esti-
mate with a combination of first name and country (Boekhout, van der Weijden, & Waltman,
2021). Some names are not possible to infer gender from, because they are very rare (insuffi-
cient statistical material), gender ambiguous (between-country ambiguity can be resolved;
Tuttavia, within-country ambiguity is problematic) or from countries with naming traditions
that do not typically use gendered personal names (per esempio., China and South Korea). In these
cases, the gender of an author is considered “unknown.” For all countries, the gender can
be inferred reliably for 70.5% of all authors. The majority of unidentified authors are from
China, meaning that analyses of gender do not apply to this country. Previous studies have
established very high representativity and reliability for other countries (per esempio., Madsen, Nielsen
et al., 2022; Nielsen, Andersen et al., 2017). The same research also established that a man-
ually estimated distribution of men and women in the “unknown” category was comparable to
that of the inferred genders (Nielsen et al., 2017; Santamaría & Mihaljević, 2018).

3.5. Field Classifications

This article uses the OECD fields of science classification, as defined in the OECD Revised
Fields of Science and Technology (OECD, 2007). The assignment of papers to fields is
achieved through the WoS journal subject category to OECD field of science translation table

1 https://www.leidenranking.com/information/indicators\#gender-indicators.
2 https://genderize.io/.
3 https://gender-api.com/.

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provided by Clarivate (2012). This classification operates on two levels, where the top level
covers the major fields natural sciences, technical sciences and engineering, medical sciences,
agricultural sciences, social sciences, and humanities and art. On the second level, 39 minor
fields are arranged below the six major fields. For a full overview of the included fields, and the
number of publications registered with each, Vedi la tabella 1.

Tavolo 1. OECD major and minor fields, and number of publications in Web of Science, 2000–2020. The following fields are intentionally
omitted in the Clarivate InCites conversion schema for WoS subject categories to OECD field: 4.04 Agricultural Biotechnology, 3.04 Health
Biotechnology, E 3.04 Other Medical Sciences

1 Natural sciences

1.1 Mathematics

1.2 Computer and information sciences

N

885,407

519,595

1.3 Physical sciences and astronomy

24,165,34

%

3.6

2.1

9.9

4 Agricultural sciences

N

4.1 Agriculture. forestry. fisheries

357,027

4.2 Animal and dairy science

4.3 Veterinary science

1.4 Chemical sciences

2,775,960

11.4

4.5 Other agricultural science

1.5 Earth and related Environmental sciences

1,298,582

5.3

1.6 Biological sciences

2,881,592

11.8

5 Social sciences

1.7 Other natural sciences

631,035

2.6

5.1 Psychology

5.2 Economics and business

2 Engineering and technology

N

%

5.3 Educational sciences

2.1 Civil engineering

2.2 Electrical engineering. Electronic

engineering. Information engineering

2.3 Mechanical engineering

2.4 Chemical engineering

2.5 Materials engineering

2.6 Medical engineering

2.7 Environmental engineering

2.8 Environmental biotechnology

2.9 Industrial biotechnology

2.10 Nano-technology

170,404

767,350

125,121

213,997

920,869

125,121

284,952

132,160

41,579

14,733

2.11 Other engineering and technologies

338,819

0.7

3.2

0.5

0.9

3.8

0.5

1.2

0.5

0.2

0.1

1.4

5.4 Sociology

5.5 Legge

5.6 Political science

5.8 Media and communication

5.9 Other social sciences

6 Humanities

6.1 History and archaeology

6.2 Languages and literature

6.3 Philosophy. ethics and religion

6.4 Arte

3 Medical and Health sciences

N

%

6.5 Other humanities

3.1 Basic medical research

3.2 Clinical medicine

3.3 Health sciences

1,590,483

6.5

4,306,049

17.7

1,031,921

4.2

Quantitative Science Studies

5.7 Social and economic geography

135,656

104,679

185,062

114,125

N

414,749

517,731

139,983

165,219

73,688

102,061

64,381

60,074

N

112,954

147,673

92,115

44,106

25,727

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Field-level differences in paper and author characteristics

There are many problems associated with journal-based classification systems, such as field
delineation and correct assignment of inter-, trans-, and multidisciplinary research published in
monodisciplinary journals. In questo caso, I argue that the broadness and wide acceptance of the
field definitions in the OECD system provides a helpful overview fitting of the aim of the article.
Tuttavia, we must be aware that the broadness of the fields also hides some interfield differ-
ences in characteristics. This is the case in psychology, Per esempio, which is a field that his-
torically as well as currently encompasses great methodological and epistemological diversity,
spanning from purely clinical psychology (closer to clinical medicine) to theoretical psychol-
ogy (closer to the humanities) and social and behavioral psychology (closer to sociology). Con
that in mind, the classification schema is still found valid, as the high level of the OECD fields
serves to give the kind of overview required for the present analysis, and the high aggregation
level and number of publications reduce problems of misassignment to mostly random errors. It
will, Tuttavia, be important to keep especially interfield differences in mind for future research.

4. RESULTS

In the following we report the descriptive results on a per-variable basis, first for article, Poi
for author characteristics. This section concludes with a principal component analysis of the
intervariable variance, and a reclustering of the OECD fields of science based on their biblio-
graphic characteristics.

4.1. Article Characteristics

4.1.1. Document types

WoS utilizes a large number of document types. Tuttavia, many of them have very low fre-
quency. In our data, 35 document types were identified, with the first six document types

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Figura 1. Proportion of document types, as classified by Web of Science, per OECD minor field of science, shown in panels per OECD major
field of science. The “Other” category covers less than 99% of all publications in total.

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Field-level differences in paper and author characteristics

accounting for more than 95% of all publications, and the first 10 accounting for more than
99%. As can be seen in Figure 1, almost all documents in the remaining 1% of publications
(con 25 categorie) are found in art, where this category (“Other”) covers 36.1% of all pub-
lications. In the natural sciences, agricultural sciences, and medical and health sciences, COME
well as engineering and technology, journal articles are the most frequent document types,
but also abstracts and proceedings are common, especially in computer and information sci-
ence, some engineering fields (especially electrical engineering), and education science. In
the humanities, but also media and communication and to some extent sociology, book
reviews play an important role (Zuccala & van Leeuwen, 2011). The exception to this is
art, as mentioned above. In the medical and health sciences, and especially in clinical
research, reviews are generally more present than in the other disciplines, although single
fields (chemical, biologico, and animal & dairy sciences, as well as psychology and medical
engineering and environmental biotechnology) also have some degree of secondary
literature.

4.1.2. Reference age

In Figure 2, we see the proportion of references in all papers of a given age up to 40 years,
where the age is the difference in publication years between the citing and the cited

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Figura 2.
of science, shown in panels per OECD major field of science. Numbers in boxes correspond to field codes in legend.

Share of references by reference age (difference between publication years of citing and cited document) for all OECD minor fields

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Field-level differences in paper and author characteristics

pubblicazione. In the figure, marker lines from the field codes (colored labels) to the curves all
meet the curves at 15 years reference age (arbitrarily chosen value for visual aid), highlighting
the field differences in these curves. In all disciplines except for health science and the human-
ities, there are considerable within-discipline differences. The humanities also have consider-
ably longer reference age curves than the other disciplines, and fields such as mathematics
and economics are closer to the fields in the humanities in this regard. The median reference
age, or cited half-life, can be read as the reference age corresponding to the 0.5 proportion of
references, and is shown over time in Figure 3.

The variation between median reference age is large within all disciplines, except medical
and health sciences and the humanities. Variations between fields are even greater and grow-
ing for almost all fields. Fields in engineering appear most stable over time, some even with
declines in median reference age in the latter part of the publication period (per esempio., mechanical
engineering and materials engineering). In the natural sciences, computer science is also sta-
ble throughout most of the period with a decline by the end, meaning computer scientists tend
to reference newer material today than earlier, which is likely explained by rapid growth in this
field. In particular, the humanities have long and growing median reference ages. The high
growth in this discipline is potentially an artificial effect of WoS coverage of this area in
particular.

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Figura 3. Annual mean value of the cited half-life or median reference age (difference between publication years of citing and cited docu-
ment), per OECD minor field of science, shown in panels per OECD major field of science. Numbers in boxes correspond to field codes
in legend.

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Field-level differences in paper and author characteristics

4.1.3. Reference list length and coverage

The length of reference lists per field is shown in Figure 4, illustrating the differences both
between and within fields, as well as the general growth in reference list length. Fields in
the medical and health sciences generally grow more slowly than other fields, and fields in
the social sciences (as well as history and archaeology) have the longest reference lists. IL
longest reference lists (mean over the entire period, σr) are found in history and archaeology
(σr = 50.8) che è 2.5 times longer than the shortest in electrical engineering and mathemat-
ics (σr = 20.4). Journal guidelines may play a role in limiting reference list lengths in some
cases (per esempio., some medical journals have traditionally limited the number of references that
could be included).

When taking into account which references refer to works included in WoS, the distribu-
tions change fundamentally, as seen in Figure 5. Where the medical and health sciences were
on the low range of the number of references, almost all of these are covered in both basic and
clinical medical research. All fields in the humanities have very low coverage. All fields here
are below mean coverage, σc = 25% and as low as 13.4% (art). In the social sciences and
economics & business, as well as psychology are considerably better covered than the other
fields, which are all covered between 25% E 50%. In both the natural sciences and the
technical and engineering sciences, there is great within-discipline variation, with well-
covered fields such as industrial biotechnology (σc = 84.4%), nanotechnology (σc = 80.2%),

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Figura 4. Median number of references per article over time, per OECD minor field of science, shown in panels per OECD major field of
science. Numbers in boxes correspond to field codes in the legend.

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Figura 5. Mean percentage of references in reference lists identifiable in Web of Science over time, per OECD minor field of science, shown
in panels per OECD major field of science. Numbers in boxes correspond to field codes in legend.

biological sciences (σc = 79.1%), and chemical sciences (σc = 79.0%), and much less covered
fields such as civil engineering (σc = 44.5%) and computer & information science (σc =
50.4%). Most fields have growing coverage over the time period, except for the humanities,
where fields either remain low in coverage or grow very slowly (per esempio., languages & literature,
and philosophy, ethics, & religion).

4.1.4. Article length

One fundamental difference between publications from different fields, even though they are
here all considered original research articles in journals, is the typical length of an article. As
illustrated in Figure 6, both medical sciences and agricultural sciences, and somewhat also
engineering and technology, are on the low end of the scale, with 5–10 pages per publication
(although increasing to a little more than 10 for some fields in recent years), while the natural
sciences spread over the middle range with 5–20 pages per publication, and mathematics and
computer science have by far the longest articles in the discipline. The humanities generally
produce the longest articles, quite closely concentrated around 20 pagine, while the social
sciences fall between 10 E 20 pagine, although law stands out as having by far the longest
articles, at 30 pages on average. Both the mean (solid lines) and median (dotted lines) are
shown in the figure, illustrating that for most fields, these two statistics are very close, while
for some (per esempio., mathematics and law), a tail of very long articles creates a mean somewhat
higher than the median.

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Figura 6. Mean (solid lines) and median (dashed lines) number of pages per article over time, per OECD minor field of science, shown in
panels per OECD major field of science. Numbers in boxes correspond to field codes in the legend.

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4.1.5. Reference density

Both the length of an article and the number of references can be seen as potential symbols of
the amount of intellectual work that has gone into an article. While there is certainly no direct
causality between these two indicators and the quality or intellectual value of an article, IL
combination of references per article page (reference density) can indicate whether articles in a
given field are more information dense than others, assuming that increased information per
article requires further references to underline arguments and give credit to prior claims, eccetera.
Figura 7 shows the changes over time in average number of references per page across OECD
fields of science, and the trends must of course be seen in relation to trends in article length
(Figura 6) and reference list length (Figura 4), of which this is a ratio. We see that fields in the
agricultural, medical and health sciences, and humanities, as well as mathematics and biolog-
ical sciences, have stable reference density or only very small changes over time, but also very
different baseline densities (from around one reference per page in mathematics to two in the
humanities and four in the agricultural, biologico, and medical and health sciences). È
unclear why the category “other natural sciences” decreases over time, except that a corre-
sponding growth in article length occurs concurrently. All other fields have rising trends, con
a growth of more than one reference per page over the period. A considerable part of this
change is likely explained by expansive growth in these fields, providing more relevant refer-
ences to include.

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Figura 7. Mean number of references per page per article over time, per OECD minor field of science, shown in panels per OECD major field
of science. Numbers in boxes correspond to field codes in legend.

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The interpretation of these values is not obvious. Tuttavia, with differences of this magni-
tude, there is no doubt that there are quite different norms for how to write articles and how to
use references across fields, with direct consequences for bibliometric evaluation. This raises
the question of whether comparisons between the most different fields, despite normalizing
citations, Per esempio, are meaningful.

4.1.6. Coauthorship

As noted in Section 2, coauthorship is one of those variables that have seen considerable
change over time. This is confirmed in Figure 8, illustrating general growth in both the mean
(solid lines) and median (dotted lines) number of coauthors per article in all fields, except for
mathematics and the humanities (which may appear to see a small growth in the most recent
years only). While the bump observed in 2010 for “other engineering and technology” is likely
an outlier, the extreme change in mean number of coauthors occurring in physics and astron-
omy from 2011 and onward is not by chance, but quite stable up until 2020. These may still to
some degree be considered outliers, as the publications underlying the change are relatively
few articles with several hundred, sometimes more than a thousand, coauthors. These articles
mainly stem from the CERN high-energy particle experiments, but there are also some astro-
physics papers. Computer science and mathematics are the only natural science fields with
less than five coauthors per paper on average in the most recent observations, and the figure

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Figura 8. Mean (solid lines) and median (dashed lines) number of coauthors per article over time, per OECD minor field of science, shown in
panels per OECD major field of science. Numbers in boxes correspond to field codes in the legend.

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is particularly low for mathematics. Fields in the social sciences and humanities are also much
less collaborative than those in other disciplines. While tradition may play a role in these dif-
ferences, it seems more likely that the growth across the hard sciences is a result of new
requirements for data collection and generation that require larger teams (big science), clearly
illustrated by the bump in coauthors for physics. While parts of the social sciences are taking
steps in the same direction, the data show there are still many studies conducted by individuals
or small teams.

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4.2. Author Characteristics

In this section, results on the author characteristics of fields are reported. All analyses shown
here are calculated on information based on the disambiguated author data described in the
metodi, and thus rely on a larger publication data set than used in the article characteristics
section. The analyses are still done on the same sample, but they use information on, for exam-
ple, total number of papers per author at the time of publishing a given paper, or an author’s
first year of publication.

4.2.1. Alphabetization

In Figure 9 we see the rate of alphabetical ordering (solid lines) and the probability of
intentional alphabetical ordering (dashed lines) according to the methodology proposed by

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Figura 9. Share of papers with alphabetical (solid lines) and probability of intentionally alphabetical (dashed lines) author sequence order
over time, per OECD minor field of science, shown in panels per OECD major field of science. Numbers in boxes correspond to field codes in
the legend.

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Waltman (2012), across OECD fields of science. The analysis only includes articles with two
or more authors. As noted in the methods, fields with low collaboration may have somewhat
high differences between the two curves, even though the rate of alphabetical ordering is
high. This is clearly the example in mathematics, which goes from 80% A 60% alphabetical
ordering, the highest of all fields, but is also one of the fields with the lowest number of coau-
thors, making it difficult to estimate statistically whether the ordering is intentional, Tuttavia,
with values as high as here, it is safe to assume that alphabetical ordering is a common but
not exclusive form of attributing authorship in mathematics. Similar statements can be made
for computer and information science, although the tendency to use alphabetical ordering is
lower and decreasing more over time. This decrease is found across most fields, but less so
in the social sciences and humanities. General trends and field differences for fields are
similar to those in Waltman (2012) and Mongeon et al. (2017), confirming previous research
on the topic.

4.2.2. Gender distribution

Figura 10 shows the proportion of authors with reliably inferred gender who are estimated to
be women, across all fields. The proportion reflects not headcount but proportion of author-
ships in a given year attributable to women. Very few fields have more than half their

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Figura 10. Proportion of women as authors per field over time, per OECD minor field of science, shown in panels per OECD major field of
science. Numbers in boxes correspond to field codes in the legend.

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authorships from women, but most of the humanities and some social sciences, as well as
health science, reach parity at the end of the period, and fields such as sociology and educa-
tion science even have more female authorships than male. In engineering and technology
and the natural and agricultural sciences, as well as in basic and clinical medical research,
most fields have low representation of women at the beginning of the period, in some cases
down to 10%, but with increasing shares over time. Fields such as mathematics, computer and
information science, nanotechnology, and civil, mechanical, and electrical engineering have
the lowest representation of women and also have very low growth. In the social sciences and
humanities, the lowest representation is found in economics and business, social and eco-
nomic geography, political science, and philosophy, ethics, and religion.

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4.2.3. Author sequence

In the following three figures, data on the productivity (Figura 11), genere (Figura 12), E
academic age (Figura 13) for first, last, and middle authors are reported. These three figures
together inform us about further demographic differences and expectations around the
meaning of authorship in academic fields. It also helps us understand which assumptions
are reasonable to make about different author positions, and which fields it is meaningful to
apply author weighting by sequence position in.

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Figura 11. Median productivity at publication year (ρy) for first (circle) and last (triangle) authors. Dashed lines show first (low endpoint) E
third (high endpoint) quartile productivity. Distributions represent the entire period 2000–2020, per OECD minor field of science, shown in
panels per OECD major field of science.

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With regard to productivity by author position, Figura 11 shows productivity variation
between first and last authors, using the productivity indicator, ρ. We see from the figure that
most fields outside of the social sciences and humanities have a wide variation between the
two positions. Evaluating this figure should take into account that data points show median
productivity and the low and high end points of the dashed bars show first and third quartile
variance. Considering this, mathematics is the only field outside the social sciences and
humanities that has no real difference between first and last author, although some other
fields only have small differences in the median productivity (per esempio., computer and earth sci-
enze, as well as civil, mechanical and environmental engineering). In the social sciences
and humanities, the only field with median productivity difference between author positions
is psychology, although several fields have some difference in the third quartile. This is an
interesting observation taken together with the trends in alphabetization, indicating that many
of these fields have a dual modus for authorship interpretation, where at times the sequence
is alphabetical, and when it is not, the last author is more likely to be more senior (higher
productivity).

Figura 12 shows both the absolute and the proportional share of female authors by author
sequence position. The absolute share is calculated in the same way as the distributions in
Figura 10 and are of course dependent on how many women are active researchers in the

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Figura 12. Percentage share and proportional share, with respect to overall distribution, of women authorships as first (circle), last (triangle),
or middle (square) author. Proportional share is the ratio between women at a given position and the ratio of women overall. Distributions
represent the entire period 2000–2020, per OECD minor field of science, shown in panels per OECD major field of science.

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different fields. The proportional share, Tuttavia, is the share at each author position com-
pared to the overall share in that field. This proportional share indicates that there are dif-
ferences in the roles and seniority of men and women across all fields. Even in fields that
tend to use alphabetical ordering, there are more women in the first author position and
fewer women in the last author position than expected, given the overall share of women.
The only three exceptions to this are law, political science, and philosophy, ethics, and reli-
gion, where only the middle author position is overrepresented by women, while the first
author position is at 1 (law) or below (political science and philosophy, ethics, and religion).
Fields with higher degrees of alphabetical ordering tend to have more narrow proportional
spread (values closer to 1).

In Figure 13, we see the academic age of authors, at the time of publishing a publication,
as either first, last, or middle author. The academic age is calculated as the number of years
between the first publication recorded for a specific author set and the current one. Così, one
individual author may be present several times in the data set, with varying ages over time.
For each field, the circles represent the median age at publication for first authors, triangles
represent last authors, and squares represent middle authors. These distributions correlate
highly with those in Figure 11, as all fields outside of the social sciences and humanities have
large differences between first and last author age, except for mathematics, although there is

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Figura 13. Median academic age (difference between first publication year of an author and current publication year) for first (circle),
last (triangle), and middle (square) authors per article. Lower and upper bounds of dashed lines show first and third quartile academic
age. Distributions represent the entire period 2000–2020, per OECD minor field of science, shown in panels per OECD major field of
science.

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a small difference here too. The other fields with smaller productivity variation are also the
fields with smaller age variation. Middle author age varies a lot between fields, indicating
that there is less certainty about their roles compared to the first and last author roles; Essi
may be both senior and early career scientists, although in most fields they tend to be closer
in age to the first author. There are no differences between author position in the humanities,
and in the social sciences the only large difference is found in psychology. Economics
and business and social and economic geography also have small differences, but only
of a few years.

4.3. Field Clustering

In the previous two sections, we have seen several bibliographical and demographic differ-
ences between fields, with variation both within and between disciplines. For some vari-
ables, individual fields have stood out, and some fields (per esempio., mathematics) have consistently
had different distributions than other fields in their discipline, while other disciplines have
had very similar distributions between their fields. To explore if these differences can be
systematically grouped, principal component analysis (PCA) is used to cluster variables.

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For this purpose, we use the mean values of all variables except for those around gender.
The gender variables were included above to inform researchers about expected popula-
tions prior to selecting fields to study, and not as a bibliometric variable or a scientific com-
munication variable, as also described in the methods section. This leaves 11 variables,
which are on very different scales. To compensate for this, PCA is performed in R, using
the built-in prcomp function, with scale = TRUE to rescale variables. In the resulting list of
components, the first two explain 78.9% of the variance between variables, as shown in
Figure 14a. To recluster fields based on their composition, the Euclidean distance between
vectors composed of the component loadings is calculated and used for hierarchical clus-
tering. A dendrogram showing these clusters of fields can be seen in Figure 14b. The num-
ber of clusters identified from this approach is a subjective but informed choice, and tests
were performed with other cluster numbers and compared to the underlying data. The first
of these comparisons is through a biplot of the variables’ loadings on the first two compo-
nents, shown in Figure 15, using the original OECD major fields to group minor fields, E
in Figure 16 showing the same fields but grouped by the clusters identified in the
dendrogram.

Figura 15 shows the loadings on the first component on the x-axis and on the second
component on the y-axis. The two factors together do not explain all the variance in the
dati, which is why the clusters shown in Figure 16 appear to diverge slightly from the clus-
ters in the dendrogram. This is most clear for the pink-colored cluster containing political
science, law, and social and economic geography (we will refer to this cluster as HumSoc,
as it is the part of sociology closest to the humanities in this plot), which in the biplot
appears to overlap with the teal SocSoc cluster (named as a social science middle ground
between the humanities and hard sciences). This apparent overlap is due to the limitations in
plotting just two components, even when they explain almost 80% of the variance.

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Figura 14. Proportion of cumulative variance explained by principal components (panel a, left), and dendrogram of field clusters based on
Euclidean distance between fields represented by component loadings.

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Figura 15. Distribution of fields based on loadings on the two components explaining the most variance (PC1 and PC2). Arrows show the
relation between field characteristics and components. Hulls around fields represent the OECD major field groupings.

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The other clusters quite neatly group the fields they consist of on the first two compo-
nents, as shown by the colored hulls around the field point estimates. The humanities are
unaffected by the regrouping, while most of the natural, medical, agricultural sciences, E
engineering and technology are grouped into one cluster. Psychology is added to this clus-
ter, due to aforementioned characteristics closer to those of clinical medicine. Calcolatore e
information science is sufficiently different from the other hard sciences to form a singleton
cluster, and mathematics and economics form a small cluster on their own.

Figura 17 confirms the split of the social sciences, with the HumSoc and SocSoc clusters
having quite different loadings on some of the components. While the hard sciences have a
considerable amount of variance in the PC1 loadings, they are also the only cluster loading
positively on this component, explaining why these fields are grouped despite the high var-
iance. All in all, this shows us that a wide range of the hard sciences can be used for quan-
titative studies of bibliometric and sociological patterns of science, and that it is reasonable
to use assumptions about author position, productivity, and reference norms in sample selec-
tion procedures. It also shows us that much more care must be used when working with
fields outside of the hard sciences, and that these assumptions do not hold here. Piuttosto,
one should assume lower coverage, less meaning in author sequence position, fewer coau-
thors, longer median reference age, and a lower share of journal articles.

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Figura 16. Distribution of fields based on loadings on the two components explaining the most variance (PC1 and PC2). Arrows show the
relation between field characteristics and components. Hulls around fields represent the clusters generated from component loadings.

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Figura 17. Distribution of component loadings per cluster as Tukey-boxplot.

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5. DISCUSSION

The descriptive analyses of article and author characteristics in this article confirm previous
research on individual fields and single measurements but bring them together in a compre-
hensive overview. The results show that from a bibliometric and sociology of science point of
view, some fields in the natural and social sciences cannot be compared to other fields in
those disciplines. This is particularly the case for mathematics, economics, computer science,
and psychology. The first three of these are very different from all other fields in general, per-
haps having most in common with each other, whereas psychology tends to look more like
clinical medicine than the other social sciences in terms of how they produce knowledge.

The data shown in this paper provide an overview of the limitations and possibilities for
bibliometric research in specific fields, and the reclustering of fields offers insights into which
fields can reasonably be grouped together in bibliometric and quantitative sociological anal-
yses of the scientific communication system. Allo stesso tempo, it raises the question of whether
it is meaningful to aggregate, Per esempio, citation scores (also field-normalized and
percentile-based ones) across fields that are as dissimilar as many of the fields are.

Summarizing the findings per characteristic, I find that

(cid:129) Articles are the main publication type in all disciplines, except for the Humanities. Some

fields use conference articles a lot, but most focus on journal articles.

(cid:129) Cited half-life has large between-field variation, but also within-field variation in most
disciplines. This is likely linked to how cumulative the knowledge production modes of
the fields are.

(cid:129) Average reference list length varies by more than a factor of two between fields and

grows considerably over time in some fields.

(cid:129) WoS coverage of references in the reference list is very low in the humanities (10–20%)
and low for many of the social sciences, as well as some technical and natural sciences.

(cid:129) Article length varies much across all fields and stays quite stable over time.
(cid:129) The number of references per page grows in some fields but is mostly stable. Humanities
and most social sciences, as well as mathematics and some engineering fields, are least
dense.

(cid:129) The humanities have very few coauthors per paper. The social sciences are becomingly
increasingly collaborative but do not have the same team size as other disciplines. Phys-
ics and astronomy have by far the highest mean number of coauthors per paper.
(cid:129) Alphabetical author lists are common in mathematics and computer science, as well as
most of the social sciences and all of the humanities; Tuttavia, due to less collaboration
in these fields, estimates are less precise.

(cid:129) Many of the natural sciences and engineering fields have very few women scientists,
while health science, some of the social sciences and parts of the humanities have
reached parity.

(cid:129) There is a strong connection between author position and the author’s number of papers
written in fields without alphabetical ordering, except in the humanities and social sci-
enze (although psychology is more like medicine in this regard).

(cid:129) Even in fields with parity, and in fields with alphabetical ordering (with few exceptions
only), it is more common for women to be first authors (more likely to be early career)
and men to be last authors (more likely to be senior).

Aggregating research to fields, based on journals, and on the levels used in this study, È
meaningful for interpreting results, but also has limitations. One problem in this respect is

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the incorrect classification of articles originating from one field but published in a journal
belonging to another field. This is especially a problem on the microlevel, and less so on
the macrolevel, as we can reasonably expect that only a low number of articles out of the
entire population are erroneously classified. More substantially, some of the included fields
cover a very broad range of methodologies and topics, and therefore also tend to communi-
cate findings differently, depending on the subfield. One of the examples that can be seen is
psicologia, which includes social, behavioral, theoretical, and clinical psychology. Also, IL
trends in coauthorship in physics mask the theoretical physics subfield(S), with much different
coauthorship patterns.

The analysis of author characteristics is, like other current large-scale analyses, limited by
the quality of the author disambiguation and gender inference algorithms and data availability.
For bibliometric researchers wanting to work with (near-)complete sets of publication and
author data, the challenge of addressing these limitations in the data is of the same significance
as addressing field differences in publishing norms. Tuttavia, the solutions to these problems
are not trivial, and should be a priority for future research. This becomes even more the case as
China becomes an increasingly important science producer, as Chinese names are also among
the most difficult to disambiguate and infer gender from, at least when using the pinyin trans-
literation seen in the majority of bibliographic databases (Sebo, 2021).

In summary, the results presented in this article reiterate concerns about both normalization
techniques and their application to aggregates composed of very diverse disciplines, and the
use of very large data sets for bibliometric analysis without controlling for field. Across all var-
iables included here, at least some fields were fundamentally different from other fields, even
within disciplines. This shows us that great care must be taken when trying to make universal
claims about science and that perhaps we should strive less for universality in these types of
studies and more for well-defined, delimited samples.

ACKNOWLEDGMENTS

I greatly appreciate the willingness of colleagues, and especially Jesper Wiborg Schneider and
Mathias Wullum Nielsen, in discussing the various elements, findings, and interpretations pre-
sented in this work.

COMPETING INTERESTS

The author has no competing interests.

FUNDING INFORMATION

This study is funded by the Independent Research Fund Denmark, grant number 0133-
00165B.

DATA AVAILABILITY

A deidentified data set of all data used for this study is available from Andersen (2023).

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