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Gender-Related Differences in the Citation Impact of Scientific Publications and Improving the Authors’ Productivity

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08 May 2023

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08 May 2023

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Abstract
The article's purpose is a citation analysis of the impact of scientific publications by authors of different gender compositions. The PageRank method was chosen to calculate the citation impact of scientific publications, and the citation has also estimated the impact of scientific publications based on the number of citations. The normalized citation impact of scientific publications is calculated according to nine subsets of scientific publications that correspond to patterns of different gender compositions of authors. Also, these estimates were calculated for each country with which the authors of the publications are affiliated. The Citation database was chosen for the scientometric analysis Network Dataset ( ver . 13). The dataset includes more than 5 million scientific publications and 48 million citations. The main subject areas of scientific publications in this database are computer science, artificial intelligence, mathematics, engineering, etc. The results indicate that articles with a predominantly male composition are cited more than articles with a mixed or female composition of authors in this direction. Analysis of advantages in dynamics indicates that in the last decade for developed countries, there has been a decrease in the connection between the citation impact of scientific publications and the gender composition of their authors. However, the obtained results still confirm the presence of gender inequality in science, which may be related to socioeconomic and cultural characteristics, natural homophily, and other factors that contribute to the appearance of gender gaps. An essential consequence of overcoming these gaps, including in science, is ensuring the rights of people in all their diversity.
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Subject: Social Sciences  -   Gender and Sexuality Studies

1. Introduction

New knowledge, ideas, and innovations are created thanks to the development of scientific cooperation. Scientific cooperation is a joint activity of scientists to create and verify new knowledge. The results of scientific cooperation are the publication of scientific publications, the organization and implementation of joint scientific projects, and the organization of conferences, seminars, and other scientific events. The increase in the productivity of the scientific activity of individual scientists and scientific teams is a factor that affects the development of innovations in the region and the state as a whole. The current direction of scientometrics is identifying the influence of demographic, social and gender differences on publishing productivity. In works [1,2], it was determined that the form and intensity of scientific cooperation affect publishing productivity and the creation of innovations [3]. This process is significantly influenced by the peculiarities of the construction of the social space in which scientific teams cooperate. It can be assumed that one of the influencing factors in forming patterns of scientific collaboration is gender. The impact of gender differences on publication productivity and citation of scientific publications is described in [4]. In work [5], it was found that gender-heterogeneous working groups allow the production of scientific results of higher quality. However, it is complicated by natural gender homophily [6]. The ability to collaborate with peers also manifests itself in citations of scientific publications. In work [7], scientists tend to cite publications by authors of the same gender as themselves. Gender-based questions about homophily in research are described in works [8,9].
Ensuring respect for human dignity, equality, and respect for human rights are critical values of the EU and other countries with a high human development index. An essential condition for ensuring these values is the implementation of a policy of gender equality and the elimination of gender gaps. Therefore, in recent decades, there has been a tendency to decrease the influence of gender differences among performers on the formation of the composition of scientific projects. In particular, work [10] indicated that the influence of gender differences on scientific publication productivity is decreasing in current conditions, especially among young scientists. The analysis in [10] claims that gender differences in the productivity of scientific activity have been disappearing recently. A few decades ago, the number of scientific publications with male authors significantly exceeded that of female authors, but now this trend has changed. However, it was difficult for women to get positions in science for a long time since this field was almost entirely male [11]. However, even with the gender representativeness of the STEM direction in education and science, this process was accompanied by increased gender differences in productivity and influence [12].
The prevailing situation is that there are fewer females than males in the higher ranks in academic circles. In work [13], it is indicated that, personally, females with high scientific results in a scientific group significantly influence the productivity of this scientific group. In work [14], it is indicated that this is influenced by the higher emotional intelligence of females compared to males. Ensuring gender diversity in educational and scientific spaces is complex and multifaceted. Some aspects of gender diversity policy in university networks are described in [15]. It is important to note that gender representativeness can differ in different science areas. In work [16], a study of the results of the work of 150,000 mathematicians was conducted. It has been shown that females publish less early in their careers and drop out of research faster than males. As a result, top mathematics journals publish fewer articles authored by women. A similar trend can be observed in the direction of computer science. However, this is a separate research task.
Even though the trend of overcoming gender gaps is one of the priorities in developed countries, questions remain as to whether scientific publications with a different gender composition are cited differently. And if so, what could it be connected with? To find answers to this question, choose a method using which you can effectively evaluate citation impact. Traditionally, citation impact is defined as the number of times subsequent publications cite a publication.
One of the methods that can be used to evaluate the scientific publication productivity or citation impact of a scientist is the PageRank method [17]. The traditional purpose of the PageRank method is to determine the influence of a user on social networks or to evaluate the importance of web pages. Each network user or page is assigned an actual number that measures importance or reputation. The larger this number, the higher the importance [18]. There are modifications to the PageRank method to calculate the productivity of scientific activities, the citation index, scientific journals' reputation, etc. The classical PageRank method uses only edge relations and does not consider higher-order structures, particularly subgraphs. One of the concepts of modifying the PageRank method, described in [19], is the complication of the evaluation calculation by including higher-order structures in the calculation. In work [19], it is shown that this approach helps perform the ranking of social network users better. This approach makes sense because citation networks tend to have a complex structure. This fact can be considered to assess the impact of citations in practice. However, it is challenging to use this method in real-time. A dynamic change in the structure of the citation network leads to the need to recalculate the scores, which is cumbersome.
In [20], an iterative method for calculating PageRank is proposed, simplifying the rating calculation. In general, using the PageRank method allows you to consider all the information about all the citations of the network authors when evaluating. While the h-index [21] and its analogs, such as the i10-index, g-index, etc., when calculating the productivity of scientific activity, lose part of the citations outside the core. The work [22] describes the method of calculating the scientific productivity of collective subjects (universities, scientific institutes, departments, faculties, etc.) based on the Time-Weighted PageRank Method with Citation Intensity (TWPR-CI). It is shown that the advantage of the TWPR-CI method is the higher sensitivity of the scientific productivity estimates for new collective subjects that it averages during the first ten years of observation. The method's sensitivity is essential and can be used for citation impact evaluation, especially for recently published posts. However, the number of citations of new publications may be small, so this method will not differ from the classic PageRank method.
An analysis of the continuity of research in intergender scientific cooperation [23] is a direction that allows a better understanding of the features of the involvement of scientists of different genders in joint scientific projects. Well-known methods of researching patterns of scientific cooperation and choosing scientists for the organization of projects [23,24] can also be used to study the influence of gender on scientific interaction. Also, the methods described in works [25,26,27,28,29,30] can be used to evaluate the productivity of scientific activity, management, and competence selection of project executors using a gender approach. The work [31] describes a thorough study of the impact of gender inequality on scientific careers in different countries. It found that the increase in female participation in science over the past 60 years has been accompanied by a widening of the gender gap in both scientific productivity and impact.
The article hypothesizes that there is a citation dependency impact of scientific publications from different gender compositions of the authors of these publications. If the effect is detected, it may mean that the gender composition of scientific teams working on joint research affects their scientific publication productivity. This trend may differ depending on the countries and areas of scientific research, and may change over time. Accordingly, the article's goal is citation analysis impact of scientific publications by authors with different gender compositions. Also, the article does not suggest that biases are conscious and that biases may depend on other socioeconomic and cultural factors but allow to reveal existing inequalities. Identified differences in the citation of scientific publications are not a sign of discrimination based on gender but are an indicator that captures the current state of publication activity.
A citation data set of scientific publications was investigated Network Dataset (13 versions) of more than 5 million scientific publications and 48 million citations [32], collected from databases such as DBLP [33], ACM [34], Microsoft Academic Graph [35], and others. The construction of the database is described in more detail in [36]. The following research stages were implemented:
  • Calculate the citation impact for each scientific publication in the citation network. For this, a method based on calculating the number of citations of scientific publications was used. Also, for citation impact calculation, the PageRank method was used [37,38].
  • All publications are divided into eight classes according to the gender composition of the authors of these publications. The publication's belonging to the corresponding cluster is determined by the author's article based on a unique service for determining the gender of a person by their first name.
  • To set the dependency citation impact of scientific publications from the gender composition of the authors of these publications, the obtained results for eight classes are compared among themselves. Special attention should also be paid to citation scores' impact on scientific publications by authors from different countries. Analyzing the change in citation scores' impact on scientific publications from different countries is also essential.
Researching the influence of gender differences on scientific publication productivity is relevant for the development of innovations and scientific production in general. The identified gender inequality in the academic circle should be eliminated at the institution of higher education or scientific research institution and the state level. An increase in the scientific publishing activity of the authors contributes to the growth of the scientific productivity of the institutions with which these authors are affiliated. The described study continues the research published in works [22,38].

2. Methods and Data

2.1. Basic Terms and Concepts

Some terms and concepts have been used in the publication. Citation impact is determined by the number of times subsequent publications cite a publication. This study used the PageRank method to calculate the citation impact of scientific publications. The citation impact of a scientific publication, which was calculated as a result, is called PageRank citation impact. Also, the traditional method of calculating their total number of citations was used to evaluate the impact of scientific publications.
The work focuses on the citation calculation impact of scientific publications with different gender compositions. This is important to understand the regional distribution by country and the change over time in the intensity of citation of scientific publications with different gender compositions: male, female, and mixed.
Patterns for the gender composition of authors were highlighted. Each pattern corresponds to a specific class in which scientific publications were included. Each of these classes is studied separately. To evaluate the citation impact of scientific publications by authors from different countries using open data collected over a long period. This allows you to investigate the change of citation impact of scientific publications for different classes over time. Also, sufficient data allows us to analyze the citations separately and the impact of scientific publications in different countries.
The work examines eight patterns for the gender composition of authors of scientific publications. It is assumed that a particular pattern will determine each article, and the citation score impact for these articles will differ. All scientific publications are divided into eight classes or subsets for each pattern separately. Let S = { s 1 , s 2 , … , s n } is the set of scientists, n is the number of scientists. Let P = { p 1 , p 2 , … , p m } is the set of scientific publications published by scientists from set S, and let m is the number of scientific publications. With each publication p j , j = 1 , m ¯ one or more authors of this publication are associated. We set the function F ⊆ S × P , which the set of pairs will determine ( s i , p j ) , i = 1 , n ¯ , j = 1 , m ¯ . Let's set the function: g : S → { f , m } determines the gender of each scientist from the set S. Then define a tuple: Δ ( p j ) = 〈 g ( s i ) | ( s i , p j ) ∈ F , i = 1 , n ¯ , j = 1 , m ¯ 〉 .
If for scientific publications p k , k = 1 , m ¯ , p k ∈ P , ∀   d ∈ Δ ( p k ) ,   d = f , c a r d ( Δ ( p k ) ) > 1 , then all authors of scientific publications p k are women and publications belong to the pattern "Fff". If c a r d ( Δ ( p k ) ) = 1 then publications belong to the pattern "F". If ∀   d ∈ Δ ( p k ) ,   d = m , c a r d ( Δ ( p k ) ) > 1 , then the authors of the scientific publications p k are male and, accordingly, the publications belongs to the "Mmm" pattern, if c a r d ( Δ ( p k ) ) = 1 , the publication belongs to the "M" pattern. Other patterns are described in Figure 1. A capital letter at the beginning of the pattern's name indicates the gender of the first author of the scientific publication, respectively F – female, M – male. The analysis of the specified number of classes or subsets of scientific publications corresponding to the specified patterns is sufficient for the study.
It should be noted that the gender composition of publications is determined based on a service that checks the gender of the authors of these publications. Separately, a significant number of publications with an uncertain gender composition should be considered, when at least for one author, the service cannot identify author’s gender with sufficient accuracy. It should also be understood that the obtained results may have some deviations since, among the authors, a certain number of persons may identify themselves as not binary. Still, the first name cannot determine it.

2.2. The Assessmalet of citation impact and PageRank citation impact of scientific publications

To calculate the citation index impact for each scientific publication, you need to calculate the number of citations of this publication in other scientific publications. This indicator shows the influence of a scientific publication. The higher the citation rate impact of a scientific publication, the greater the influence of this publication. If Q C I = { q 1 , q 2 , … , q m } is the citation scores impact for each scientific publication p j , j = 1 , m ¯ , Q C I : P → ℕ ∪ { 0 } . This indicator only shows the total number of citations, but it can quantify this publication's interest among other relevant authors.
PageRank method to evaluate the influence of scientific publications. This method allows you to determine the impact of a scientific publication in comparison with other publications under consideration. According to the PageRank method, the scalar evaluation of the citation impact of a scientific publication p j is j = 1 , m ¯ calculated according to the formula:
r j = ∑ y = 1 m β j y ξ y r y ,   j = 1 , m ¯ ,
where is r j the PageRank score citation impact of a scientific publication p j , j = 1 , m ¯ , β j y , j = 1 , m ¯ , y = 1 , m ¯ the coefficient that determines the presence of a scientific publication p j in j = 1 , m ¯ the list of publication citations p y , y = 1 , m ¯ , ξ y is a coefficient that ensures the existence of a non-trivial solution of the system of linear algebraic equations (1).
As a result of applying formula (1), a homogeneous system of linear algebraic equations is constructed:
B r = 0 ,
where B is the matrix of coefficients of the system of the form :
B = Е − { β j y ξ y } j , y = 1 m ,
where E is the single matrix, r = w T is a column vector unknown of grades, w = ( r 1 , r 2 , … , r m ) For there to be a non-trivial solution of the system of algebraic equations (1), the matrix B must be degenerate, i.e., det ( B ) = 0 .
Let's ask a subset of the Cartesian product С ⊂ P × P , which determines the citation of publications P × P = { ( p j , p y ) |   p j , p y ∈ P , j ≠ y } . Plural scientific publications which cited by a given publication p j ∈ P we define through С ( p j ) = { p y ∈ P   |   ( p j , p y ) ∈ C ,   y = 1 , m ¯ } . The formulas can determine the coefficients of system (1):
β j y = { 1 ,   i f   p j ∈ C ( p y ) 0 ,   i f   p j ∉ C ( p y ) ,
ξ y = ‖ C ( p y ) ‖ − 1 ,   y = 1 , m ¯ ,
where β j y is the indicator of the presence of the publication p j in the list of publication references p y , ξ y is the value inverse of the total number of citations in the publication p y .
After finding the estimates, it is advisable to standardize them according to the formula
r ′ ( p i ) = r i ( ∑ j = 1 m r j ) − 1 ,   i = 1 , m ¯ ,
where r i is the PageRank score citation impact of a scientific publication p i , i = 1 , m ¯ , r ′ ( p i ) is the normalized PageRank score citation impact of a scientific publication p i , i = 1 , m ¯ .
The more citations a scientific publication has over time, the higher its citation impact. Therefore, to evaluate the citation impact of a scientific publication, you can count the number of citations of this publication. The advantage of calculating the citation score impact of a scientific publication index using the PageRank method is that this method considers the influence of a scientific publication by the number of citations compared with the citations of other scientific publications.
The citation base of scientific publications was analyzed in the Network Dataset (ver. 13), and a citation network was built. Next, the citation score was calculated for all scientific publications based on the number of citations and PageRank rating citation impact of all scientific publications. It is necessary to solve the system of linear algebraic equations of large dimensions (2) to find the PageRank score citation impact. The iterative process of the Gauss- Seidel method is used to find the approximate solution of the system of linear algebraic equations (2). At step zero, the value of the PageRank scores citation impact of all scientific publications is equal to 1. At the k-th step, the value of each PageRank score citation impact The formula to find the index of the publication:
r j k = ∑ y = 1 m β j y ξ y r y k − 1 ,   j = 1 , m ¯ ,   k ∈ â„• ,
where r j k is the approximate value of PageRank citation impact publications p j at the k-th step, r j k − 1 is the approximate value of the PageRank estimate citation impact publications p j at the (k-1)-th step, and the coefficients are calculated according to formulas (3), (4).
After each step, starting from zero, the maximum relative change in citation scores was calculated to impact scientific publication according to the formula:
Δ k = max j = 1 , m ¯ | r j k − r j k − 1 | ,
where Δ k is the maximum relative change in PageRank scores citation impact scientific publication p j . The iterative method stops if ∃   ε > 0 the maximum relative change in citation scores impacts scientific publication Δ k < ε . The value ε > 0 is some small number that is specified in advance. After that, the values are normalized according to the formula (5).
A method for determining the gender composition of authors of scientific publications is proposed. The conceptual diagram of the method is shown in Figure 1. The method consists of three stages.
At the preparatory stage, PageRank scores are calculated for each scientific publication’s citation impact and citation impact by the number of citations.
In the first stage, the gender identity of the authors is determined by their names using the genderize.io service [39]. This service allows you to determine with the specified accuracy whether the entered first name belongs to a male or female. First is used to determine the gender name of each author. If the name belongs to a male's name according to the genderize.io service (identification accuracy threshold exceeds 0.9), then the author is identified as a man. If the name belongs to a female, according to the genderize.io service (identification accuracy threshold exceeds 0.9), the corresponding author is identified as a female. If the identification accuracy threshold is less than 0.9, then we believe the author's gender cannot be determined. The threshold is chosen empirically since the gender of the author should be identified as accurately as possible. As already indicated, among the authors of publications, there may be a small part of those who, according to the genderize.io service, are identified as male or female, but they are not binary. Determining this fact by the first name is impossible.
In the second stage, the set of scientific publications with the known gender of the authors is divided into eight subsets (Table 1). If the gender of at least one of the authors could not be determined, then the article belongs to the subset with an uncertain gender composition of authors. Each author of a scientific publication has a specific affiliation. Accordingly, the publication belongs to those countries whose authors are affiliated with institutions of higher education or scientific institutions of these countries.
From the database of scientific publications, Citation Network The dataset was selected from those scientific publications affiliated with the list of countries with different gender parity scores according to the Global Gender Gap Report 2022 [40]. This is necessary to check whether there is a correlation between citation scores impact of scientific publications by authors from certain countries on their gender parity score, according to the Global Gender Gap Report 2022.
Also, to establish the dynamics of changes in the citation rating impact of scientific publications of different countries over time, their evaluations were calculated for two patterns with purely male and female authors.
Jupiter notebook environment was used for scientometric analysis and data set processing in Python programming language.

3. Results

3.1. Collection of Data

The database of Citation publications was used for the scientometric analysis of the Network Dataset (ver. 13) of 5,354,309 scientific publications and 48,227,950 citations [32], collected from databases DBLP [33], ACM [34], Microsoft Academic Graph [35], and others. The specified version contains current data on publication citations as of May 2021.
The research used data that other researchers partially pre-processed. In particular, the considered dataset does not contain duplicate publications. Unique identifiers are assigned to each researcher and each publication. Also, only the authors' full names and their countries of affiliation were used in the study. The probability of spelling errors in these data is minimal. We also manually checked randomly selected data samples.
When determining the gender of the author, we avoided controversial points. If the genderize.io service did not indicate the gender with sufficient probability, we marked the gender of the author as unknown.
For scientometric analysis, the entire database analyzed scientific publications in English from 1815 to 2021; however, publications and bases were unevenly distributed over time. About 90% are scientific publications published from 1998 to 2021. The quantity of publications in the Citation Network Dataset (ver. 13) by decades is shown in Figure 2.
The subject areas of the publications in this database were studied separately. The central part of publications belongs to such subject areas as computer science, artificial intelligence and artificial neural networks, mathematics and discrete mathematics, optimization and combinatorics, and software engineering. The cloud of subject directions is shown in Figure 3. This study analyzed the data comprehensively, and the distribution was not carried out separately according to these directions. For visualization, data by the subject was selected, including more than 200,000 publications. Belonging to the subject area was determined by the FOS parameter from the Citation database Network Dataset (Table 2). It should be noted that a scientific publication can belong to several subject areas simultaneously.
It can be assumed that, depending on the subject area to which scientific publications belong, the gender composition of the authors of these publications may differ. In addition, citing such publications from various subject areas may have certain features. However, this is a separate research task requiring more data from other subjects.
The patterns of the gender composition of the authors of these publications are defined in Table 1, and services for identifying male and female first names were used. The genderize.io service was used To compile lists of male and female first names. The genderize.io contains data on the potential gender of 114,541,298 first names from 242 countries worldwide. Among the authors of publications in Citation Network, 451,052 unique first names were identified in the dataset, for which the gender affiliation of the authors was determined using the genderize.io service. As a result, it was established that among the authors of publications, there are 86,792 female names, 193,747 male names, and 170,513 names, the gender of which could not be established with a reliability of more than 90%. As a result of applying this method, the gender identity of all authors was established for 76.6% of publications in the selected data set. For 23.4% of publications, it was not possible to establish gender affiliation for at least one of the authors.
To determine the gender of the authors, the use of the Gender API [41] service, which contains data on 6,084,389 first names from 191 countries, was also considered, but this service offers only 100 requests per month for free use. Therefore, it was selected for control. Namely: among all 280,539 first names of scientific publications, for which the gender of the authors was determined using the genderize.io service, 100 were randomly selected, for which the gender of the authors was determined using the Gender API service. In all 100 cases, gender identity coincided, which makes it possible to assert the sufficient reliability of the proposed method.
The space character separates author’s full name into words to select the first author's name. Next, a search is done for each word in the list of names without considering the case of the letters. If the author's first name is not in the list of names according to the genderize.io service or only the initials are indicated, then it is considered that the gender of the author could not be established. In addition to the subsets specified in Table 1, one more subset must be constructed. This subset will include the remaining scientific publications and the gender of the authors, which could not be established by the specified method (NA).
His affiliation was determined to establish the author's affiliation with a specific country. A publication belongs to a subset of publications from a particular country if at least one of the authors is affiliated with a higher education institution belonging to that country.

3.2. The Results of the Calculation of PageRank citation impact index and citation impact index by the number of citations

Citation database Network Dataset was calculated by their citation impact according to the PageRank method and taking into account the number of citations. The accuracy of the iterative PageRank method has been established in citation impact ε = 10 − 4 . The maximum relative change in PageRank citation impact of a scientific publication is considered the upper estimate of the absolute error of the method. After performing six iterations of calculating the impact rating of publications, the absolute error was Δ 6 = 2 , 48 * 10 − 5 (7). The authors consider this estimation accuracy sufficient, so the calculation process was completed Δ 6 < ε . A citation score was also calculated to impact scientific publications by their citation in other publications. According to this method, all scientific publications in the database are reviewed, and the number of citations of one publication in others is recorded. This number will determine the citation impact of a scientific publication
After calculating the citation scores impact of scientific publications among all publications from the data set, data on publications from countries for which the research hypothesis is tested were filtered. Next, the gender identity of the authors of these publications was determined using the genderize.io service. As a result of the research, the gender identity of all authors was established for 76.6% of publications. For 23.4% of publications, it was not possible to establish gender affiliation for at least one of the authors. For each country, publications were divided into subsets according to the patterns described in the table. 2. Table 3 shows the number of scientific publications whose authors are affiliated with the specified 12 countries. Data for all countries are given in Appendix A. According to the Citation database, two countries with a small number of scientific publications were included in this table Network Dataset for comparison with other countries, the significantly higher number of publications.
Also, the dataset was examined to fulfill the diverse requirements within the proposed subsets defined by the defined patterns. For this, the normalized Shannon entropy was calculated using the formula:
H = − 1 log 2 W ∑ v = 1 W m v m log 2 m v m ,
where H is the normalized Shannon entropy, m v is the power of the subsets of scientific publications according to the patterns in Table 2 and the subset for which it was impossible to determine the gender composition of the authors of the publications (N / A), v = 1 , W ¯ , W = 9 , m is the total number of publications. It is established that for Citation Network Data (ver. 13), H=0.7197. The such indicator indicates sufficient representativeness of the sample.
It is observed that for most countries, the subsets determined by patterns Mmm, M should include more publications than pattern subsets Fff, F. The requirements of the project, according to which the study was carried out, required the inclusion of research information on the countries of Kazakhstan and Ukraine. The selection of articles for Kazakhstan and Ukraine is not representative, but the general trend regarding the gender composition of the authors of the publications is visible. For each subset that corresponds to the relevant patterns of gender composition and the subset with an uncertain gender composition of authors and selected countries, the impact of scientific publications was calculated by the PageRank method and by the number of citations. Normalized citation scores' impact is given in Table 4 and Table 5.
The results of a pairwise comparison of publications from the represented countries from different subsets according to different patterns, on average, indicate that scientific publications with the first author, who is male or with a predominantly male composition of authors, have higher citation scores impact compared to publications whose authors are primarily female (Table 6). The specified trend is preserved for citation estimates impact, calculated by the number of citations and citation impact by the PageRank method. A feature has been established that the maximum number of citations of scientific publications by subset with the pattern Mmm is higher than that of scientific publications from subsets with other patterns of the gender composition of authors for most of the indicated countries. A negative value in Table 6 indicates that the specified advantage of the estimates of the two subsets is reversed. If the value of preferences in Table 6 is closer to zero, there is a bias in the citation estimates and no impact. Accordingly, scientific publications with a male and female gender composition are mainly evaluated equally.
The change in relative PageRank scores was calculated for citation impact for the period up to 2010 and from 2010 to 2021 to understand how the specified preferences change over time. The value of the benefits was determined as the difference between the average normalized ratings of the respective patterns divided by the maximum of the values. The trend of rating changes was also considered, and PageRank citation impact was determined according to different patterns. Figure 4 shows the trends of changes in the values of the evaluations of advantages F ≺ M , F f f ≺ M m m for different countries comprehensively by publications from four subsets, which patterns F, M, Fff, and Mmm determine. Such subsets of scientific publications were explicitly selected to highlight scientific publications with a purely male or female composition of authors. For subsets F f m ≺ M f m , F m m ≺ M f f , which can be seen from Table 6, preferences vary in different countries, and this change is also traced over different periods.
Table 7 shows the pairwise comparison of relative PageRank scores citation impact of scientific publications from different research areas according to defined patterns. The scores in the table are indicated for the areas represented by the most significant number of publications in the dataset. The research hypothesis is confirmed for all the indicated directions.
Such results can be connected to many socioeconomic factors, such as female representation in science, cultural characteristics, etc. As can be seen from Figure 4, over the last decade, the citation rate impact for scientific publications with a purely male composition of authors decreased compared to the citation impact of publications with a purely female composition of authors. In most countries in the last decade, there has been an increase in the influence of women in science and the representation of women in scientific research, which is published in the best scientific journals. However, the state of equilibrium, i.e., the approach of preference estimates to zero, has yet to be reached for any country.
Estimates of the preferences of subsets with different patterns by calculated citation impact can determine the availability of opportunities for females and males to participate in scientific projects and publish high-quality scientific articles. It can be assumed that in developed countries, for specific estimates of benefits F ≺ M , F f f ≺ M m m the value will be close to zero. This means that publications with a female and male composition are cited equally. Accordingly, the representation of females and males in science is equally high.

4. Discussion

4.1. Findings

The estimates of citation impact may, to some extent, reflect the productivity of the authors of these publications. The more the author's publications are cited, the more author is published in the best scientific journals. Accordingly, for such an author, there will be faster career growth in science and will be more invited to participate in scientific projects, etc. There is a "closed circle" effect here. If the author's publications are poorly cited, the career growth of such an author will be slower.
Since two performance assessment methods were used, the correlation coefficient between all assessments was calculated for their comparison. The correlation coefficient calculated between the estimates by the PageRank method and the number of citations equals 0.754. The correlation coefficient was also calculated for non-zero scores, equal to 0.647. This makes it possible to argue that the methods provide related but not functionally dependent estimates. Since relative evaluations are used for comparison, the different number of scientific publications from different patterns affects the evaluation result.
As you know, the participation of females in science is complicated, mainly due to pregnancy, the need to devote more time to raising children, and the greater representativeness of males in the management of scientific projects. Even a short-term pause in scientific activity can affect the dynamics of career growth in this direction, publication of high-quality scientific papers, research in scientific projects, etc. It can become more acute in different cultures and according to the socioeconomic status of the countries. Accordingly, this direction depends on ensuring gender equality in the country.
Based on the results, it can be concluded that scientific publications with male authors are cited more. Accordingly, their scientific publication productivity will be higher. It is established that the citation impact of a scientific publication depends on the gender composition of its authors. This means that the gender composition of scientific teams working on joint research affects their scientific publication productivity. Considering the superiority of publications with a male composition over publications with a female composition, we can conclude gender inequality. That is, the scientific publication productivity of female authors in these conditions will be lower than male authors.
However, the dynamics of evaluations of the advantages of subsets according to the defined patterns of the top ten countries by publication representation in the Citation Database Network Data show an overall improvement in gender equality in science.
Citation scores impacted scientific publications by certain countries' authors' gender parity scores, according to the Global Gender Gap Report 2022 [40]. It was established that the correlation coefficient is -0.168, which indicates a weak anti-correlation. This can be explained by the fact that the gender parity score refers to all aspects that affect gender equality in a country. In this study, only the aspect of scientific activity is considered, particularly one of its components: publication activity and citation of scientific publications. In addition, many other socioeconomic and cultural factors influence the equal representation of females and males in science and their scientific results.

4.2. Limitations and Future Research Lines

A limitation of the study is that in the Citation database Network Dataset, most publications relate to the subject area of natural sciences. Accordingly, the presentation of scientific publications in the social sciences or humanities could be more extensive. It is possible that, for publications in a non-naturalist subject area, value evaluations of the citation impact of scientific publications will differ from those calculated in this research. Also, note that the number of citations to scientific publications in some countries may influence the results received.
Another limitation is the impossibility of setting authors from not binary gender since identifying whether the author is male or female was made based on their first names.
The more citations a given article receives over time, the higher its influence and the higher the author's productivity. Accordingly, one of the directions of future research is the assessment of aspects of the organization of project teams with different gender compositions on the productivity of each team member and the team's results as a whole. Also, an essential aspect of future research is to show the dynamics of changes in the evaluations of the preferences of subsets according to the corresponding patterns. In addition, the specified patterns can be considered patterns of scientific collaborations. This can be singled out as a separate indicator for assessing gender equality in scientific activity in different countries, regions, universities, etc. The research aims to inform countries, universities, and scientific institutes of problems related to gender gaps in science and to find ways to overcome them.

5. Conclusions

The work analyzed the citation impact of scientific publications by authors with different gender compositions. The PageRank method was used for citation impact evaluation of scientific publications and calculating the number of citations of scientific publications. The estimated citation impact of publications is calculated for different countries by eight subsets of publications that correspond to the patterns of the gender composition of their authors. The citation score is also calculated impact for the case when the gender composition of the authors of a scientific publication cannot be identified. The advantages of evaluations for subsets corresponding to different patterns are calculated.
Based on the Citation Network Dataset, results of the citation evaluation impact of scientific publications with mostly male authors indicate that the citation impact of publications with a female composition prevails over the citation impact of publications with a female composition. It indicates that articles from mainly male authors are cited more than articles with a mixed or female composition of authors. Analysis advantages in dynamics indicate that in the latter decade, there was a reduced influence of the gender composition of the authors' publications on citation impact. This may be the result of gender equality policies in many countries. However, the obtained results still confirm the existence of gender inequality in science, which may result from cultural and socioeconomic factors or natural homophily.
The obtained results can be considered more broadly. Author groups are often established, and the same author groups publish different publications in their direction. This means that citation scores are obtained impact of scientific publications with different gender compositions of authors corresponds to the assessment of the productivity of different gender patterns of scientists in scientific collaborations in different countries. This is important for intensifying the debate in the direction of ensuring gender equality and overcoming gender gaps in science. An increase in the scientific publishing activity of the authors contributes to the growth of the scientific productivity of the institutions with which these authors are affiliated. The obtained results do not mean the presence of discrimination based on gender, and the results indicate the peculiarities of citing scientific publications with different gender compositions. However, the intensity of citations of such publications can be influenced by various socioeconomic, cultural, and other factors.
Appendix A (Table A1, Table A2 and Table A3) the power of subsets of publications that correspond to the patterns of their gender composition. The average normalized PageRank scores indicated the citation impact of scientific publications by several citations for countries with more than 100 authors affiliated.

Author Contributions

Conceptualization and methodology, OK and YA; software, YA; analysis, OK, YA, SB, and AF; coding, YA; writing–original draft preparation, OK and YA; writing–review and editing, AB, SO and. AM; visualization OK and. YA; project administration, AB. All authors have read and agreed to the published version of the manuscript.

Funding

This paper was written in the framework of the state order to implement the science program for budget program 217 "Development of Science", IRN No. BR18574103 with the topic: "To increase the competitiveness of universities in Kazakhstan through the reengineering of the national system of quality assurance of higher education".

Data Availability Statement

All data are available in this publication. The data used to generate the figures in this article are available in Appendix A. Publicly available datasets analyzed in this study can be found here: Citation Network Dataset: DBLP+Citation , ACM Citation network. (2021). Aminer. Retrieved from: https://www.aminer.org/citation (accessed on April 1, 2023).

Acknowledgments

Acknowledgmalets: The authors thank the reviewers and editors for their generous and constructive comments that have improved this paper.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Power of subsets of posts that match patterns of their gender composition (data for countries with more than 100 authors).
Table A1. Power of subsets of posts that match patterns of their gender composition (data for countries with more than 100 authors).
Country Pattern
Count N/A Fff Mff Fmm Mmm Ffm Mfm F M
USA 442281 7430 17259 33253 156798 19740 54625 9153 45685 98338
China 412520 5542 13062 32203 80288 30370 75899 3298 9127 162731
Germany 162127 1167 4019 10598 72292 5175 18475 3467 27713 19221
France 123725 1633 4106 9972 42829 6126 17662 4410 18075 18912
Japan 110524 412 1940 7775 59387 2189 11719 792 10749 15561
Great Britain 103727 1311 3413 7782 34887 4104 11192 3186 15937 21915
Italy 98243 2473 4456 9336 33108 8485 19035 1740 5824 13786
India 96816 2394 3830 8103 27083 3443 8251 1007 4024 38681
Canada 94056 1546 3982 8290 36520 3670 10620 1547 7974 19907
Spain 81132 1157 2553 6638 29979 5373 15076 567 2824 16965
Australia 59920 973 2193 4850 21038 2968 7780 1227 5736 13155
Taiwan 59137 323 961 1373 3992 527 1449 694 2040 47778
Brazil 44463 772 1730 3127 17394 2659 7897 1188 3381 6315
Netherlands 43988 558 1270 3558 16374 2274 5370 686 4152 9746
South Korea 42562 328 950 2653 14760 919 3897 288 1569 17198
Iran 32109 354 1052 4127 13627 1079 2789 201 1427 7453
Singapore 30578 255 927 2246 8086 1197 3720 232 1109 12806
Hong Kong 29945 257 880 2107 7366 1091 3344 301 1263 13336
Poland 29603 530 1297 2217 12600 850 2701 1108 6072 2228
Switzerland 29296 237 768 2466 13575 1194 4160 383 2728 3785
Israel 27091 598 1320 2514 11522 1006 3066 677 3067 3321
Greece 26867 227 703 2220 12430 986 3392 205 1594 5110
Sweden 26577 519 952 2171 11204 1148 3073 664 3159 3687
Turkey 26471 794 1686 2484 9904 997 2297 622 2818 4869
Austria 25093 229 637 1740 12206 933 3152 382 2782 3032
Belgium 24671 271 693 1935 10513 1264 3647 335 2079 3934
Finland 22618 604 722 1890 8364 1449 3286 598 2462 3243
Portugal 22132 455 794 1897 10002 1441 3376 250 1024 2893
Georgia 20110 368 747 1516 7160 912 2593 426 1954 4434
Russia 18801 279 794 1226 5293 719 2190 451 2465 5384
Denmark 15055 250 454 1222 6412 679 2031 347 1941 1719
Mexico 15044 169 486 1150 5567 680 2415 148 971 3458
Czech Republic 13746 110 479 775 7105 251 1396 289 1942 1399
Ireland 13360 181 434 1317 5644 694 1871 212 1072 1935
Malaysia 13353 405 602 918 2845 925 1945 90 267 5356
Norway 13206 246 457 1163 5291 553 1580 334 1629 1953
New Zealand 9889 158 416 900 3444 489 1306 211 1091 1874
Pakistan 9777 63 214 1057 4248 562 1570 40 286 1737
Saudi Arabia 8998 262 242 517 3542 234 675 147 1113 2266
Hungary 8487 48 274 523 4098 157 667 169 1490 1061
Tunisia 8475 528 243 2048 2057 1536 782 115 228 938
Romania 8429 262 494 948 2392 664 1097 336 1012 1224
Egypt 8042 123 291 758 2604 567 805 128 699 2067
South Africa 6947 206 365 544 2184 214 518 180 712 2024
Chile 6314 44 226 323 3385 238 910 36 395 757
Algeria 5849 197 253 891 2031 417 745 73 252 990
Thailand 5807 176 287 521 1141 250 441 128 311 2552
Slovenia 5032 96 231 491 2002 293 749 107 465 598
Argentina 4859 197 227 483 1634 466 808 76 261 707
Morocco 4659 89 67 769 1617 372 504 27 123 1091
Serbia 4445 171 222 468 1381 417 820 103 463 400
Colombia 4180 38 133 345 1795 231 766 30 152 690
Vietnam 4104 15 94 181 1243 75 352 38 255 1851
UAE 3895 49 153 430 1388 135 445 66 501 728
Jordan 3524 37 141 245 1497 126 494 56 452 476
Croatia 3334 113 186 339 1224 206 479 80 233 474
Slovakia 3129 88 276 330 1047 119 445 116 352 356
Luxembourg 3028 19 57 281 1449 117 547 40 225 293
Cyprus 2949 53 100 300 1301 122 335 43 258 437
Bulgaria 2690 155 197 329 510 198 348 174 353 426
Qatar 2467 24 62 267 1067 118 366 12 92 459
Bangladesh 2275 34 38 95 332 77 124 18 55 1502
Indonesia 2266 71 102 203 549 152 283 31 83 792
Lebanon 2099 36 83 259 824 135 300 24 205 233
Macedonia 2058 55 113 254 760 168 328 28 126 226
Peru 2049 47 98 170 786 133 387 39 106 283
Ukraine 1981 47 89 106 509 142 367 29 245 447
Estonia 1822 35 67 179 727 107 237 59 210 201
Lithuania 1768 44 106 135 639 79 244 40 246 235
Kuwait 1405 32 55 84 521 22 124 38 260 269
Latvia 1251 102 116 92 281 86 123 80 179 192
Ecuador 1190 18 35 106 412 116 309 6 36 152
Philippines 1046 31 58 109 266 99 189 37 65 192
Niger 1041 13 39 63 325 35 111 17 140 298
Nigeria 1032 13 38 63 319 35 111 17 138 298
Mongolia 968 19 25 86 176 72 164 19 31 376
Iraq 958 22 38 56 411 37 130 12 86 166
Cuba 943 22 30 85 306 88 174 6 20 212
Venezuela 936 28 51 85 298 49 103 12 71 239
Uruguay 887 18 36 68 405 56 122 14 72 96
Iceland 808 17 35 44 340 44 142 17 75 94
Montenegro 718 27 36 69 196 36 101 23 132 98
Oman 704 2 25 46 269 11 40 16 116 179
Malta 687 2 30 70 324 21 84 7 73 76
Sri Lanka 620 17 15 59 84 38 48 4 15 340
Kazakhstan 607 22 21 52 129 57 74 3 72 177
Macau 582 8 19 41 148 23 69 18 17 239
Belarus 572 7 26 43 153 5 69 11 53 205
Puerto Rico 483 5 14 27 178 21 63 5 52 118
Saint Martin 445 14 11 25 172 32 52 12 42 85
Ethiopia 380 1 12 25 168 3 43 3 35 90
Small 364 1 11 17 122 10 37 4 42 120
Kenya 324 3 19 22 100 24 62 7 23 64
Armaleia 318 5 15 16 99 6 34 9 40 94
Cameroon 315 2 11 13 94 14 36 5 18 122
Azerbaijan 310 7 13 7 81 7 23 3 37 132
Bosnia and Herzegovina 302 13 17 35 89 29 51 6 24 38
Palestine 301 0 12 17 138 3 40 2 57 32
Ghana 299 1 8 20 146 1 36 3 37 47
Costa Rica 265 10 14 19 99 14 45 5 31 28
Bahrain 247 6 5 17 62 8 17 12 55 65
Senegal 194 0 6 20 72 7 28 0 12 49
Brunei 193 1 8 6 33 10 18 3 13 101
Uganda 187 2 6 25 59 13 32 8 14 28
Myanmar 187 29 26 30 17 8 7 2 8 60
Mauritius 184 7 6 18 23 6 6 1 8 109
Libya 171 1 7 10 65 4 11 2 18 53
Fiji 168 0 11 12 60 9 23 4 17 32
Panama 167 4 9 13 67 8 29 4 11 22
Paraguay 161 0 2 16 91 9 28 0 4 11
Jamaica 157 1 13 16 44 3 20 8 16 36
Albania 150 2 5 22 39 17 37 3 4 21
Tanzania 144 1 8 12 43 7 13 3 23 34
Benin 138 3 6 14 38 2 20 3 14 38
Moldova 134 7 6 2 64 6 16 0 18 15
Liechtenstein 125 0 3 10 64 3 22 1 11 11
Yemale 118 0 6 10 42 0 13 1 16 30
Botswana 117 0 6 5 35 2 6 1 7 55
Sudan 112 4 2 24 33 4 14 4 7 20
Namibia 111 12 7 16 15 9 18 9 10 15
Syria 105 2 2 18 39 3 10 0 11 20
Trinidad and Tobago 102 1 4 15 30 1 2 11 21 17
Table A2. Average normalized PageRank scores citation impact of scientific publications for countries with which more than 100 authors are affiliated.
Table A2. Average normalized PageRank scores citation impact of scientific publications for countries with which more than 100 authors are affiliated.
Country Pattern
Count N/A Fff Mff Fmm Mmm Ffm Mfm F M
USA 442281 1.000 0.515 0.714 0.732 0.881 0.562 0.727 0.460 0.775
China 412520 0.989 0.676 0.786 0.809 1.000 0.669 0.829 0.758 0.970
Germany 162127 1.000 0.462 0.528 0.570 0.722 0.510 0.649 0.301 0.377
France 123725 1.000 0.876 0.892 0.694 0.923 0.600 0.737 0.299 0.497
Japan 110524 1.000 0.602 0.757 0.629 0.745 0.482 0.702 0.709 0.764
Great Britain 103727 1.000 0.957 0.836 0.861 0.997 0.818 0.878 0.476 0.582
Italy 98243 1.000 0.531 0.646 0.638 0.821 0.546 0.642 0.513 0.775
India 96816 0.882 0.568 0.739 0.595 0.835 0.639 0.778 0.736 1.000
Canada 94056 1.000 0.711 0.668 0.642 0.836 0.605 0.719 0.578 0.939
Spain 81132 1.000 0.672 0.874 0.784 0.931 0.675 0.832 0.603 0.771
Australia 59920 1.000 0.812 0.687 0.708 0.946 0.658 0.768 0.649 0.797
Taiwan 59137 0.581 0.499 0.564 0.491 1.000 0.374 0.439 0.560 0.609
Brazil 44463 1.000 0.662 0.655 0.763 0.838 0.655 0.741 0.196 0.296
Netherlands 43988 1.000 0.453 0.675 0.616 0.761 0.502 0.648 0.453 0.728
South Korea 42562 1.000 0.445 0.602 0.479 0.780 0.502 0.636 0.334 0.737
Iran 32109 1.000 0.669 0.833 0.716 0.784 0.722 0.805 0.603 0.575
Singapore 30578 1.000 0.461 0.508 0.521 0.633 0.415 0.550 0.453 0.629
Hong Kong 29945 1.000 0.543 0.732 0.661 0.884 0.462 0.802 0.436 0.700
Poland 29603 1.000 0.426 0.595 0.750 0.802 0.468 0.626 0.524 0.731
Switzerland 29296 1.000 0.653 0.591 0.710 0.765 0.500 0.580 0.904 0.692
Israel 27091 1.000 0.435 0.540 0.533 0.721 0.442 0.600 0.620 0.649
Greece 26867 1.000 0.716 0.937 0.688 0.767 0.656 0.784 0.874 0.886
Sweden 26577 1.000 0.512 0.589 0.557 0.713 0.480 0.585 0.456 0.693
Turkey 26471 1.000 0.517 0.701 0.618 0.781 0.605 0.609 0.456 0.673
Austria 25093 1.000 0.495 0.772 0.619 0.783 0.599 0.710 0.525 0.752
Belgium 24671 1.000 0.486 0.866 0.854 0.918 0.568 0.745 0.555 0.767
Finland 22618 0.852 0.416 0.621 0.537 0.867 0.542 0.720 0.494 1.000
Portugal 22132 0.916 0.639 1.000 0.727 0.732 0.766 0.677 0.732 0.982
Georgia 20110 0.949 0.653 0.990 0.802 0.937 0.745 0.783 0.781 1.000
Russia 18801 0.798 0.544 0.688 1.000 0.742 0.537 0.631 0.636 0.784
Denmark 15055 0.767 0.490 0.754 0.621 0.916 0.565 0.961 0.382 1.000
Mexico 15044 1.000 0.422 0.444 0.452 0.507 0.401 0.492 0.260 0.457
Czech Republic 13746 1.000 0.403 0.812 0.689 0.830 0.497 0.720 0.412 0.695
Ireland 13360 0.565 0.349 1.000 0.498 0.528 0.355 0.996 0.282 0.468
Malaysia 13353 0.718 0.168 0.709 0.436 0.588 0.348 0.534 0.427 1.000
Norway 13206 1.000 0.343 0.633 0.499 0.738 0.553 0.682 0.429 0.626
New Zealand 9889 1.000 0.450 0.638 0.689 0.878 0.542 0.851 0.463 0.923
Pakistan 9777 1.000 0.450 0.449 0.302 0.482 0.367 0.376 0.230 0.513
Saudi Arabia 8998 0.766 0.578 0.621 0.663 1.000 0.529 0.550 0.509 0.835
Hungary 8487 1.000 0.368 0.578 0.408 0.795 0.375 0.559 0.360 0.759
Tunisia 8475 1.000 0.511 0.593 0.634 0.839 0.631 0.744 0.528 0.840
Romania 8429 0.657 0.226 1.000 0.364 0.649 0.408 0.486 0.226 0.663
Egypt 8042 1.000 0.342 0.439 0.592 0.732 0.447 0.556 0.405 0.662
South Africa 6947 0.899 0.643 0.619 0.824 0.877 0.674 1.000 0.313 0.605
Chile 6314 0.772 0.655 0.582 0.587 0.814 0.503 0.622 0.479 1.000
Algeria 5849 0.981 0.341 0.892 0.674 0.817 0.453 0.843 0.100 1.000
Thailand 5807 1.000 0.334 0.370 0.900 0.975 0.449 0.766 0.364 0.755
Slovenia 5032 1.000 0.575 0.758 0.769 0.972 0.699 0.795 0.563 0.832
Argentina 4859 1.000 0.520 0.939 0.609 0.905 0.405 0.820 0.678 0.905
Morocco 4659 0.297 1.000 0.208 0.157 0.311 0.121 0.199 0.203 0.627
Serbia 4445 0.573 0.725 0.754 0.677 0.792 0.636 0.795 1.000 0.596
Colombia 4180 1.000 0.269 0.483 0.549 0.478 0.454 0.438 0.811 0.760
Vietnam 4104 1.000 0.471 0.409 0.734 0.925 0.578 0.563 0.500 0.834
UAE 3895 0.747 0.371 0.661 0.684 1.000 0.690 0.952 0.422 0.735
Jordan 3524 0.769 0.325 0.529 0.705 1.000 0.413 0.745 0.586 0.385
Croatia 3334 1.000 0.236 0.342 0.507 0.708 0.333 0.518 0.137 0.696
Slovakia 3129 0.794 0.406 0.507 0.595 0.481 0.419 0.495 1.000 0.505
Luxembourg 3028 1.000 0.566 0.706 0.969 0.639 0.301 0.499 0.694 0.748
Cyprus 2949 0.987 0.543 0.518 0.729 0.881 0.947 0.775 0.564 1.000
Bulgaria 2690 1.000 0.483 0.422 0.378 0.693 0.303 0.670 0.538 0.629
Qatar 2467 1.000 0.370 0.391 0.906 0.621 0.679 0.635 0.372 0.743
Bangladesh 2275 0.870 0.175 0.650 1.000 0.723 0.438 0.672 0.428 0.524
Indonesia 2266 0.590 0.108 0.214 0.299 0.540 0.263 0.288 0.597 1.000
Lebanon 2099 0.868 0.140 0.393 0.298 1.000 0.464 0.562 0.846 0.672
Macedonia 2058 1.000 0.234 0.522 0.657 0.792 0.761 0.863 0.118 0.926
Peru 2049 0.724 0.575 0.653 0.450 0.803 0.431 0.620 0.042 1.000
Ukraine 1981 0.996 0.621 0.324 0.468 1.000 0.818 0.654 0.640 0.533
Estonia 1822 0.900 0.050 0.198 1.000 0.604 0.839 0.850 0.316 0.715
Lithuania 1768 0.789 0.240 0.777 0.448 1.000 0.421 0.534 0.327 0.949
Kuwait 1405 0.936 0.548 0.439 0.174 1.000 0.419 0.442 0.182 0.545
Latvia 1251 0.255 1.000 0.297 0.167 0.164 0.131 0.134 0.029 0.146
Ecuador 1190 1.000 0.458 0.197 0.571 0.551 0.602 0.612 0.000 0.084
Philippines 1046 0.720 0.000 0.380 0.525 0.516 0.000 0.827 1.000 0.466
Niger 1041 1.000 0.135 0.758 0.262 0.885 0.156 0.368 0.138 0.920
Nigeria 1032 1.000 0.039 0.457 0.073 0.453 0.341 0.588 0.073 0.467
Mongolia 968 0.694 0.354 0.286 0.310 0.622 0.155 0.521 1.000 0.153
Iraq 958 0.649 0.000 1.000 0.236 0.185 0.144 0.224 0.623 0.078
Cuba 943 0.268 1.000 0.279 0.151 0.499 0.083 0.355 0.536 0.258
Venezuela 936 0.366 0.000 1.000 0.319 0.533 0.358 0.603 0.000 0.010
Uruguay 887 0.648 0.129 0.606 1.000 0.439 0.828 0.584 0.670 0.454
Iceland 808 0.159 0.000 1.000 0.094 0.187 0.128 0.240 0.009 0.110
Montenegro 718 0.655 0.000 0.960 0.484 1.000 0.551 0.602 0.000 0.202
Oman 704 0.801 0.000 0.076 1.000 0.263 0.012 0.622 0.140 0.332
Malta 687 0.191 0.000 0.000 0.572 0.966 0.448 1.000 0.031 0.062
Sri Lanka 620 0.857 0.000 0.618 0.157 0.681 0.023 0.159 0.000 1.000
Kazakhstan 607 0.297 0.589 0.000 0.722 1.000 0.231 0.256 0.249 0.497
Macau 582 0.280 0.165 0.418 0.932 0.128 0.322 0.665 0.177 1.000
Belarus 572 0.427 0.051 0.000 0.260 1.000 0.048 0.117 0.000 0.694
Puerto Rico 483 0.662 1.000 0.452 0.000 0.480 0.347 0.351 0.011 0.582
Saint Martin 445 1.000 0.132 0.000 0.304 0.297 0.101 0.489 0.000 0.244
Ethiopia 380 0.524 0.000 0.000 0.080 0.168 1.000 0.073 0.000 0.075
Small 364 0.906 0.000 0.000 1.000 0.693 0.000 0.855 0.000 0.090
Kenya 324 0.179 0.000 0.000 0.000 0.332 0.852 1.000 0.000 0.000
Armaleia 318 0.651 0.000 0.000 0.250 0.216 0.000 1.000 0.000 0.608
Cameroon 315 0.219 0.000 0.000 0.000 0.241 1.000 0.000 0.000 0.142
Azerbaijan 310 0.000 0.000 0.017 0.030 0.134 0.000 1.000 0.000 0.000
Bosnia and Herzegovina 302 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000
Palestine 301 0.292 0.000 1.000 0.000 0.005 0.211 0.309 0.000 0.000
Ghana 299 0.361 0.000 0.000 0.283 1.000 0.000 0.827 0.000 0.000
Costa Rica 265 0.092 0.000 1.000 0.000 0.026 0.000 0.000 0.000 0.138
Bahrain 247 0.567 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000
Senegal 194 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Brunei 193 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Uganda 187 1.000 0.515 0.714 0.732 0.881 0.562 0.727 0.460 0.775
Myanmar 187 0.989 0.676 0.786 0.809 1.000 0.669 0.829 0.758 0.970
Mauritius 184 1.000 0.462 0.528 0.570 0.722 0.510 0.649 0.301 0.377
Libya 171 1.000 0.876 0.892 0.694 0.923 0.600 0.737 0.299 0.497
Fiji 168 1.000 0.602 0.757 0.629 0.745 0.482 0.702 0.709 0.764
Panama 167 1.000 0.957 0.836 0.861 0.997 0.818 0.878 0.476 0.582
Paraguay 161 1.000 0.531 0.646 0.638 0.821 0.546 0.642 0.513 0.775
Jamaica 157 0.882 0.568 0.739 0.595 0.835 0.639 0.778 0.736 1.000
Albania 150 1.000 0.711 0.668 0.642 0.836 0.605 0.719 0.578 0.939
Tanzania 144 1.000 0.672 0.874 0.784 0.931 0.675 0.832 0.603 0.771
Benin 138 1.000 0.812 0.687 0.708 0.946 0.658 0.768 0.649 0.797
Moldova 134 0.581 0.499 0.564 0.491 1.000 0.374 0.439 0.560 0.609
Liechtenstein 125 1.000 0.662 0.655 0.763 0.838 0.655 0.741 0.196 0.296
Yemale 118 1.000 0.453 0.675 0.616 0.761 0.502 0.648 0.453 0.728
Botswana 117 1.000 0.445 0.602 0.479 0.780 0.502 0.636 0.334 0.737
Sudan 112 1.000 0.669 0.833 0.716 0.784 0.722 0.805 0.603 0.575
Namibia 111 1.000 0.461 0.508 0.521 0.633 0.415 0.550 0.453 0.629
Syria 105 1.000 0.543 0.732 0.661 0.884 0.462 0.802 0.436 0.700
Trinidad and Tobago 102 1.000 0.426 0.595 0.750 0.802 0.468 0.626 0.524 0.731
Table A3. Average normalized estimates of citation impact by the number of citations of scientific publications for countries with which more than 100 authors are affiliated.
Table A3. Average normalized estimates of citation impact by the number of citations of scientific publications for countries with which more than 100 authors are affiliated.
Country Pattern
Count N/A Fff Mff Fmm Mmm Ffm Mfm F M
USA 442281 1.000 0.542 0.729 0.778 0.946 0.662 0.871 0.386 0.647
China 412520 1.000 0.620 0.710 0.759 0.993 0.641 0.832 0.613 0.709
Germany 162127 1.000 0.453 0.536 0.653 0.842 0.653 0.846 0.267 0.352
France 123725 0.892 0.428 0.887 0.676 0.902 0.637 0.741 0.252 1.000
Japan 110524 1.000 0.550 0.652 0.617 0.766 0.507 0.772 0.618 0.642
Great Britain 103727 0.911 0.878 0.801 0.808 1.000 0.828 0.933 0.378 0.525
Italy 98243 1.000 0.565 0.700 0.672 0.904 0.596 0.698 0.511 0.785
India 96816 0.875 0.514 0.756 0.596 0.897 0.712 0.861 0.668 1.000
Canada 94056 1.000 0.757 0.677 0.671 0.901 0.709 0.863 0.577 0.874
Spain 81132 1.000 0.643 0.909 0.817 0.934 0.757 0.841 0.436 0.713
Australia 59920 1.000 0.801 0.656 0.681 1.000 0.707 0.811 0.470 0.757
Taiwan 59137 0.581 0.440 0.570 0.477 1.000 0.417 0.464 0.457 0.544
Brazil 44463 1.000 0.563 0.596 0.733 0.836 0.640 0.735 0.132 0.259
Netherlands 43988 1.000 0.493 0.741 0.678 0.803 0.557 0.706 0.433 0.707
South Korea 42562 1.000 0.623 0.723 0.700 0.904 0.877 1.000 0.343 0.657
Iran 32109 1.000 0.315 0.526 0.431 0.718 0.551 0.609 0.253 0.544
Singapore 30578 1.000 0.694 0.840 0.716 0.741 0.744 0.837 0.566 0.447
Hong Kong 29945 1.000 0.597 0.867 0.876 1.000 0.704 0.955 0.364 0.547
Poland 29603 1.000 0.340 0.408 0.485 0.643 0.375 0.575 0.315 0.497
Switzerland 29296 1.000 0.520 0.753 0.700 0.954 0.532 1.000 0.380 0.730
Israel 27091 1.000 0.449 0.623 0.913 0.952 0.615 0.815 0.481 0.685
Greece 26867 1.000 0.670 0.603 0.746 0.853 0.523 0.583 0.472 0.635
Sweden 26577 1.000 0.421 0.606 0.590 0.777 0.446 0.787 0.558 0.599
Turkey 26471 1.000 0.816 0.853 0.674 0.787 0.752 0.805 0.679 0.722
Austria 25093 1.000 0.501 0.714 0.685 0.867 0.649 0.762 0.428 0.672
Belgium 24671 1.000 0.517 0.763 0.661 0.849 0.616 0.642 0.336 0.564
Finland 22618 0.852 0.552 0.759 0.638 0.784 0.647 0.819 0.913 0.740
Portugal 22132 0.916 0.487 0.810 0.901 0.973 0.546 0.760 0.496 0.794
Georgia 20110 0.949 0.671 0.830 0.785 0.818 0.441 0.791 0.200 0.467
Russia 18801 0.798 0.401 0.414 0.535 0.817 0.480 1.000 0.266 0.599
Denmark 15055 0.767 0.556 1.000 0.736 0.964 0.675 0.805 0.650 0.890
Mexico 15044 1.000 1.000 0.925 0.724 0.665 0.759 0.622 0.533 0.688
Czech Republic 13746 1.000 0.240 0.700 1.000 0.752 0.483 0.657 0.393 0.620
Ireland 13360 0.565 0.592 0.714 0.642 0.774 0.636 0.738 0.305 0.588
Malaysia 13353 0.718 0.557 0.871 0.656 0.916 0.603 0.968 0.286 1.000
Norway 13206 1.000 0.301 0.932 0.729 0.961 0.532 0.838 0.276 0.595
New Zealand 9889 1.000 0.120 1.000 0.211 0.201 0.135 0.395 0.095 0.140
Pakistan 9777 1.000 0.142 0.807 0.501 0.718 0.373 0.629 0.254 1.000
Saudi Arabia 8998 0.766 0.222 0.547 0.457 0.694 0.505 0.655 0.275 0.440
Hungary 8487 1.000 0.554 0.502 0.404 0.584 0.770 0.566 0.173 0.442
Tunisia 8475 1.000 0.287 0.615 0.677 0.831 0.542 0.909 0.323 0.766
Romania 8429 0.657 0.420 0.493 0.585 1.000 0.478 0.524 0.389 0.657
Egypt 8042 1.000 0.444 0.644 0.435 0.919 0.424 0.653 0.317 0.718
South Africa 6947 0.899 0.767 0.510 0.589 0.852 0.602 0.764 0.446 0.646
Chile 6314 0.772 0.055 1.000 0.208 0.454 0.183 0.346 0.104 0.484
Algeria 5849 0.981 0.270 0.360 0.525 0.685 0.389 0.469 0.129 0.453
Thailand 5807 1.000 0.555 0.568 0.809 0.920 0.891 0.954 0.261 0.622
Slovenia 5032 1.000 0.376 0.529 0.825 0.762 0.640 0.633 0.462 0.667
Argentina 4859 1.000 0.678 0.699 0.719 1.000 0.607 0.774 0.413 0.798
Morocco 4659 0.297 0.194 0.463 0.488 0.736 0.399 1.000 0.008 0.772
Serbia 4445 0.573 0.367 0.485 0.683 0.811 0.681 0.708 0.312 0.822
Colombia 4180 1.000 0.228 0.265 0.641 0.805 0.390 0.680 0.212 0.338
Vietnam 4104 1.000 0.288 0.415 0.699 0.966 0.596 0.812 0.267 0.535
UAE 3895 0.747 0.285 1.000 0.444 0.654 0.272 0.664 0.587 0.611
Jordan 3524 0.769 1.000 0.202 0.144 0.300 0.119 0.181 0.142 0.494
Croatia 3334 1.000 0.850 0.766 0.861 0.952 0.848 1.000 0.871 0.505
Slovakia 3129 0.794 0.108 0.397 0.319 0.411 0.331 0.328 0.402 0.429
Luxembourg 3028 1.000 0.483 0.550 0.867 0.639 0.552 0.626 0.175 0.433
Cyprus 2949 0.987 0.376 0.407 0.685 0.818 0.270 0.862 0.876 0.583
Bulgaria 2690 1.000 0.479 0.433 0.789 0.983 0.507 0.461 0.486 1.000
Qatar 2467 1.000 0.055 0.481 0.784 0.800 0.785 0.967 0.432 0.585
Bangladesh 2275 0.870 0.205 0.698 0.632 1.000 0.752 0.908 0.439 0.279
Indonesia 2266 0.590 0.350 0.439 0.776 1.000 0.233 0.765 0.527 0.466
Lebanon 2099 0.868 0.139 0.353 0.398 0.729 0.338 0.444 0.089 0.459
Macedonia 2058 1.000 0.123 0.365 0.653 0.725 0.350 0.382 0.339 0.319
Peru 2049 0.724 0.283 0.399 0.678 0.437 0.436 0.444 1.000 0.386
Ukraine 1981 0.996 0.312 0.349 0.650 1.000 0.257 0.832 0.474 0.490
Estonia 1822 0.900 0.292 0.500 0.575 0.868 0.504 0.494 0.465 1.000
Lithuania 1768 0.789 0.069 0.376 0.553 0.764 0.140 0.681 0.618 0.877
Kuwait 1405 0.936 0.304 0.498 0.350 0.879 0.212 0.675 0.404 0.556
Latvia 1251 0.255 0.373 0.387 0.948 0.772 1.000 0.549 0.362 0.697
Ecuador 1190 1.000 0.034 0.444 1.000 0.724 0.498 0.645 0.808 0.740
Philippines 1046 0.720 0.176 0.181 0.257 0.638 0.553 0.301 0.716 1.000
Niger 1041 1.000 0.353 0.105 0.122 0.463 0.247 0.246 1.000 0.249
Nigeria 1032 1.000 0.353 0.108 0.122 0.470 0.247 0.246 1.000 0.251
Mongolia 968 0.694 0.147 0.534 0.773 0.897 0.568 0.807 0.091 1.000
Iraq 958 0.649 0.058 0.254 0.735 0.549 0.544 1.000 0.053 0.265
Cuba 943 0.268 0.334 0.466 0.369 0.766 0.550 0.550 0.000 1.000
Venezuela 936 0.366 0.121 0.059 0.062 0.116 0.188 0.106 0.078 0.059
Uruguay 887 0.648 0.273 0.362 0.515 1.000 0.493 0.455 0.227 0.857
Iceland 808 0.159 0.998 0.423 0.158 1.000 0.444 0.480 0.173 0.583
Montenegro 718 0.655 0.045 0.245 0.533 0.464 0.278 0.589 0.102 0.427
Oman 704 0.801 0.000 0.150 1.000 0.405 0.195 0.422 0.219 0.391
Malta 687 0.191 1.000 0.250 0.146 0.276 0.190 0.219 0.045 0.131
Sri Lanka 620 0.857 1.000 0.400 0.460 0.577 0.658 0.491 0.000 0.276
Kazakhstan 607 0.297 0.072 0.011 0.427 1.000 0.293 0.143 0.569 0.215
Macau 582 0.280 0.156 0.508 0.773 1.000 0.283 0.817 0.255 0.907
Belarus 572 0.427 0.056 0.097 0.139 0.069 1.000 0.094 0.094 0.032
Puerto Rico 483 0.662 0.307 0.501 0.781 0.725 0.082 0.720 0.000 0.391
Saint Martin 445 1.000 0.334 1.000 0.641 0.704 0.316 0.654 0.307 0.327
Ethiopia 380 0.524 0.000 0.135 0.210 0.638 0.125 0.337 1.000 0.054
Small 364 0.906 0.064 1.000 0.181 0.658 0.109 0.422 0.032 0.504
Kenya 324 0.179 0.067 0.417 0.015 0.523 0.346 1.000 0.000 0.240
Armaleia 318 0.651 0.264 0.919 0.897 0.508 0.378 1.000 0.294 0.349
Cameroon 315 0.219 0.250 0.879 0.013 0.505 0.298 0.681 1.000 0.167
Azerbaijan 310 0.000 1.000 0.308 0.128 0.873 0.222 0.320 0.179 0.906
Bosnia and Herzegovina 302 0.000 0.004 1.000 0.661 0.532 0.462 0.077 0.036 0.407
Palestine 301 0.292 0.000 0.080 0.194 1.000 0.000 0.291 0.000 0.395
Ghana 299 0.361 0.154 0.327 0.040 0.063 1.000 0.072 0.013 0.024
Costa Rica 265 0.092 0.263 0.260 0.064 1.000 0.115 0.795 0.566 0.580
Bahrain 247 0.567 0.005 0.015 0.050 0.120 1.000 0.077 0.027 0.073
Senegal 194 1.000 0.000 1.000 0.119 0.271 0.643 0.384 0.000 0.010
Brunei 193 1.000 0.000 0.591 0.801 1.000 0.986 0.810 0.462 0.301
Uganda 187 1.000 0.126 0.042 0.211 0.240 0.208 0.398 0.055 0.552
Myanmar 187 0.989 0.253 0.108 0.058 0.038 0.243 0.494 0.000 1.000
Mauritius 184 1.000 0.144 0.042 0.187 0.725 0.267 0.140 0.926 1.000
Libya 171 1.000 0.000 0.932 0.302 0.364 0.000 0.393 0.000 0.217
Fiji 168 1.000 0.000 1.000 0.094 0.169 0.189 0.262 0.000 0.107
Panama 167 1.000 0.054 0.262 1.000 0.429 0.767 0.460 0.440 0.394
Paraguay 161 1.000 0.000 0.142 0.206 1.000 0.283 0.583 0.000 0.000
Jamaica 157 0.882 0.056 0.032 0.324 0.058 0.056 1.000 0.042 0.078
Albania 150 1.000 0.000 0.048 0.568 0.481 0.495 1.000 0.000 0.060
Tanzania 144 1.000 0.000 1.000 0.340 0.201 0.163 0.641 0.000 0.525
Benin 138 1.000 0.191 0.039 0.138 1.000 0.000 0.179 0.475 0.863
Moldova 134 0.581 0.124 0.094 0.000 0.394 0.194 0.293 0.000 1.000
Liechtenstein 125 1.000 0.000 0.743 0.425 1.000 0.008 0.622 0.000 0.292
Yemale 118 1.000 0.542 0.729 0.778 0.946 0.662 0.871 0.386 0.647
Botswana 117 1.000 0.620 0.710 0.759 0.993 0.641 0.832 0.613 0.709
Sudan 112 1.000 0.453 0.536 0.653 0.842 0.653 0.846 0.267 0.352
Namibia 111 1.000 0.428 0.887 0.676 0.902 0.637 0.741 0.252 1.000
Syria 105 1.000 0.550 0.652 0.617 0.766 0.507 0.772 0.618 0.642
Trinidad and Tobago 102 1.000 0.878 0.801 0.808 1.000 0.828 0.933 0.378 0.525

References

  1. Franceschet, M., & Costantini, A. (2010). The effect of scholarly collaboration on the impact and quality of academic papers. Journal of Informatics , 4(4), 540–553. [CrossRef]
  2. Cartes -Velasquez, R., & Manterola, C. (2017). Impact of collaboration on research quality: A case analysis of dental research. International. Journal of Information Science and Managemalet, 15(1), 89–93.
  3. Talke, K., Salomo, S., & Kock, A. (2011). Top managemalet team diversity and strategic innovation orientation: The relationship and consequences for innovativeness and performance. Journal of Product Innovation Managemalet, 28(6), 819–832.
  4. Maddi, A., & Gingras, Y. (2021). Gender diversity in research teams and citation impact in economics and managemalet. Journal of Economic Surveys, 35(5), 1381–1404. [CrossRef]
  5. Campbell, LG, Mehtani, S., Dozier, ME, & Rinehart, J. (2013). Gender-heterogeneous working groups produce higher quality science. PloS one, 8(10), Article e79147.
  6. Holman, L., & Morandin , C. (2019). Researchers collaborate with same-gendered colleagues more often than expected across the life sciences. PloS one, 14(4), Article e0216128.
  7. Ghiasi , G., Mongeon, P., Sugimoto, C., & Larivière, V. (2018). Gender homophily in citations. Proc. 3rd International Conference on Science and Technology Indicators, STI 2018, 1519–1525.
  8. Gender-based homophily in research: A large-scale study of man-female collaboration.
  9. Gender in the global research landscape.
  10. Van Arensberger , P., van der Weijden , I., & van den Besselaar , P. (2012). Gender differences in scientific productivity: a persistent phenomaleon? Scientometrics , 93, 857–868. [CrossRef]
  11. Two to tango? Gender differences in the decisions to publish and coauthor.
  12. Historical comparison of gender inequality in scientific careers across countries and disciplines.
  13. Zhang, M., Zhang, G., Liu, Y., Zhai, X., & Han, X. (2020). Scientists' genders and international academic collaboration: An empirical study of Chinese universities and research institutes. Journal of Informetrics , 14(4), Arctic 101068.
  14. Joseph, DL, & Newman, DA (2010). Emotional intelligence: An integrative meta-analysis and cascading model. Journal of Applied Psychology, 95(1), 54–78.
  15. Timmers, TM, Willemsen, TM, & Tijdens , KG (2010). Gender diversity policies in universities: A multi-perspective framework of policy measures. Higher Education, 59(6), 719–735.
  16. The effect of gender in the publication patterns in mathematics.
  17. Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, 30, 107–117. [CrossRef]
  18. Leskovec, J., Rajaraman, A., & Ullman, JD (2014). Mining of Massive Datasets. Palo Alto, 513.
  19. Zhao, H., Xu, X., Song, Y., Lee, DL, Chen, Z., & Gao, H. (2018). Ranking Users in Social Networks With Higher-Order Structures. Proc. AAAI Conference on Artificial Intelligence, 32(1), 232–239. [CrossRef]
  20. Bianchini, M., Gori, M., & Scarselli, F. (2005). Inside pagerank . Proc. ACM Transactions on Internet Technology (TOIT), 5(1), 92–128. [CrossRef]
  21. Hirsch, JE (2005). An index to quantify an individual's scientific research output. PNAS, 102 (46), 16569–16572. [CrossRef]
  22. Kuchansky , A.; Biloshchytskyi, A.; Andrashko , Y.; Biloshchytska , S.; Faizullin , A. The Scientific Productivity of Collective Subjects Based on the Time-Weighted PageRank Method with Citation Intensity. Publications 2022, 10, 40. [CrossRef]
  23. Shen, H., Xie, J. & Cheng, Y. (2022). The continuity and citation impact of scientific collaboration with different gender composition. Journal of Informetrics , 16 (1), Article 101248. [CrossRef]
  24. Xu, H., Kuchansky , A., & Gladka , M. (2021). Devising an individually oriented method for selection of scientific activity subjects for implemaleting scientific projects based on scientometric analysis. Eastern-European Journal of Enterprise Technologies, 6(3 (114)), 93–100. [CrossRef]
  25. Fu, F., Hauert, C., Nowak, MA, & Wang, L. (2008). Reputation-based partner choice promotes cooperation in social networks. Physical Review E 78, Arcilce 026117. [CrossRef]
  26. Biloshchytskyi, A., Kuchansky , A., Andrashko , Y., Biloshchytska , S., Kuzka , O., & Terentyev , O. (2017). Evaluation methods of the results of scientific research activity of scientists based on the analysis of publication citations. Eastern-European Journal of Enterprise Technologies, 3(2(87), 4–10. [CrossRef]
  27. Biloshchytskyi, A., Biloshchytska , S., Kuchansky , A., Bielova , O., & Andrashko , Y. (2018). Infocommunication system of scientific activity managemalet on the basis of project-vector methodology. Proc. 14th Intern. Conference on Advanced Trends in Radioelectronics , Telecommunications and Computer Engineering, 200–203. [CrossRef]
  28. Biloshchytskyi, A., Kuchansky , A., Paliy, S., Biloshchytska , S., Bronin , S., Andrashko , Y., Shabala, Y., & Vatskel , V. (2018). Developmalet of technical component of the methodology for project vector managemalet of educational environmalets. Eastern-European Journal of Enterprise Technologies, 2 (2-92), 4–13. 2018. [CrossRef]
  29. Mulesa , O., Geche , F., Batyuk , A., & Myronyuk , I. (2018). Using a systematic approach in the process of the assessmalet problem analysis of the staff capacity within the health care institution. Proc. International Scientific and Technical Conference on Computer Sciences and Information Technologies, 1, 177–180.
  30. Bushuyev , D., Bushuieva , V., Kozyr , B., & Ugay , A. (2020). Erosion of competencies of innovative digitization projects. Scientific Journal of Astana IT University, 1, 70–83. [CrossRef]
  31. Huang, J., Gates, AJ, Sinatra, R., & Barabasi , A.-L. (2020). Historical comparison of gender inequality in scientific careers across countries and disciplines. Proc. PNAS, 117 (9), 4609–4616. [CrossRef]
  32. [dataset] Aminer (2021). Citation Network Dataset: DBLP+Citation , ACM Citation network, v13. Retrieved from: <https://www.aminer.org/citation>. Accessed July 17, 2022.
  33. Dblp . (2022). DBLP. Computer science bibliography. Retrieved from: <https://dblp.org/>. Accessed July 17, 2022.
  34. Association for Computing Machinery. Available online: https://www.acm.org/ (accessed on 10 August 2022).
  35. Microsoft Academic Graph. Available online: https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/ (accessed on 10 August 2022).
  36. Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., & Su , Z. (2008). ArnetMiner : Extraction and Mining of Academic Social Networks. Proc. Fourteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD'2008), 990 – 998.
  37. Biloshchytskyi, A., Kuchansky , A., Andrashko , Y., & Biloshchytska , S. (2020). Use of the link ranking method to evaluate scientific activities of scientific space subjects. Scientific Journal of Astana IT University, 1, 12–20. [CrossRef]
  38. Biloshchytskyi, A., Kuchansky, A., Andrashk , Y., & Gladka , M. (2022). Impact of gender on publication productivity and scientific collaboration. Proc. 2022 Smart Information Systems and Technologies (SIST), 400–403.
  39. genderize.io . (2023) . Determine the gender of a name. Retrieved from: <https://genderize.io/>. Accessed April 01, 2023.
  40. Global Gender Gap Report 2022. (2022). Insight Report – July 2022. Retrieved from: < https://www.weforum.org/reports/global-gender-gap-report-2022/ >. Accessed April 01, 2023.
  41. Gender API. (2023). Gender API. Retrieved from: < https://gender-api.com/>. Accessed April 01, 2023.
Figure 1. Conceptual diagram of the method of determining the gender composition of authors of scientific publications.
Figure 1. Conceptual diagram of the method of determining the gender composition of authors of scientific publications.
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Figure 2. Number publications by decade based on Citation Network Dataset.
Figure 2. Number publications by decade based on Citation Network Dataset.
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Figure 3. Distribution publications by subject area for Citation Network Dataset database.
Figure 3. Distribution publications by subject area for Citation Network Dataset database.
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Figure 4. Change in the values of the preference estimates F ≺ M and F f f ≺ M m m for different countries.
Figure 4. Change in the values of the preference estimates F ≺ M and F f f ≺ M m m for different countries.
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Table 1. Patterns of scientific publications by the gender composition of their authors.
Table 1. Patterns of scientific publications by the gender composition of their authors.
Pattern Interpretation
Fff all authors of a scientific publication are female (more than one author)
Mmm all authors of a scientific publication are male (more than one author)
Fmm all authors of the scientific publication are male except for the first author, who is female
Mff all authors of the scientific publication are female except for the first author, who is male
Ffm authors of scientific publications, both male and female. The first author is female
Mfm the authors of the scientific publication are both male and female. The first author is male
F the scientific publication has one female author
M the scientific publication has one male author
Table 2. Number of scientific publications by different subject areas, according to the Citation database Network Dataset (displayed data by subject area with more than 200,000 publications).
Table 2. Number of scientific publications by different subject areas, according to the Citation database Network Dataset (displayed data by subject area with more than 200,000 publications).
Subject area Count
Computer science 3152625
Artificial intelligence 953033
Mathematics 845068
Algorithm 387218
Engineering 325129
Computer vision 306614
Computer network 300346
Control theory 259662
Table 3. Gender composition of authors of scientific publications by specified countries.
Table 3. Gender composition of authors of scientific publications by specified countries.
No Country All Fff Mff Fmm Mmm Ffm Mfm F M
1 USA 442281 7430 17259 33253 156798 19740 54625 9153 45685
2 China 412520 5542 13062 32203 80288 30370 75899 3298 9127
3 Germany 162127 1167 4019 10598 72292 5175 18475 3467 27713
4 France 123725 1633 4106 9972 42829 6126 17662 4410 18075
5 Japan 110524 412 1940 7775 59387 2189 11719 792 10749
6 G. Britain 103727 1311 3413 7782 34887 4104 11192 3186 15937
7 Italy 98243 2473 4456 9336 33108 8485 19035 1740 5824
8 India 96816 2394 3830 8103 27083 3443 8251 1007 4024
9 Canada 94056 1546 3982 8290 36520 3670 10620 1547 7974
10 Spain 81132 1157 2553 6638 29979 5373 15076 567 2824
11 Ukraine 1988 46 91 104 509 144 369 29 245
12 Kazakhstan 952 12 12 32 118 24 90 11 84
Table 4. Normalized relative citation scores impact of scientific publications, determined by the number of citations.
Table 4. Normalized relative citation scores impact of scientific publications, determined by the number of citations.
No Country N/A Fff Mff Fmm Mmm Ffm Mfm F M
1 USA 1.000 0.542 0.729 0.778 0.946 0.662 0.871 0.386 0.647
2 China 1.000 0.620 0.710 0.759 0.993 0.641 0.832 0.613 0.709
3 Germany 1.000 0.453 0.536 0.653 0.842 0.653 0.846 0.267 0.352
4 France 0.892 0.428 0.887 0.676 0.902 0.637 0.741 0.252 1.000
5 Japan 1.000 0.550 0.652 0.617 0.766 0.507 0.772 0.618 0.642
6 G. Britain 0.911 0.878 0.801 0.808 1.000 0.828 0.933 0.378 0.525
7 Italy 1.000 0.565 0.700 0.672 0.904 0.596 0.698 0.511 0.785
8 India 0.875 0.514 0.756 0.596 0.897 0.712 0.861 0.668 1.000
9 Canada 1.000 0.757 0.677 0.671 0.901 0.709 0.863 0.577 0.874
10 Spain 1.000 0.643 0.909 0.817 0.934 0.757 0.841 0.436 0.713
11 Ukraine 0.996 0.312 0.349 0.650 1.000 0.257 0.832 0.474 0.490
12 Kazakhstan 0.297 0.072 0.011 0.427 1.000 0.293 0.143 0.569 0.215
Table 5. Normalized relative PageRank scores citation impact of scientific publications.
Table 5. Normalized relative PageRank scores citation impact of scientific publications.
No Country N/A Fff Mff Fmm Mmm Ffm Mfm F M
1 USA 1.000 0.515 0.714 0.732 0.881 0.562 0.727 0.460 0.775
2 China 0.989 0.676 0.786 0.809 1.000 0.669 0.829 0.758 0.970
3 Germany 1.000 0.462 0.528 0.570 0.722 0.510 0.649 0.301 0.377
4 France 1.000 0.876 0.892 0.694 0.923 0.600 0.737 0.299 0.497
5 Japan 1.000 0.602 0.757 0.629 0.745 0.482 0.702 0.709 0.764
6 G. Britain 1.000 0.957 0.836 0.861 0.997 0.818 0.878 0.476 0.582
7 Italy 1.000 0.531 0.646 0.638 0.821 0.546 0.642 0.513 0.775
8 India 0.882 0.568 0.739 0.595 0.835 0.639 0.778 0.736 1.000
9 Canada 1.000 0.711 0.668 0.642 0.836 0.605 0.719 0.578 0.939
10 Spain 1.000 0.672 0.874 0.784 0.931 0.675 0.832 0.603 0.771
11 Ukraine 0.996 0.621 0.324 0.468 1.000 0.818 0.654 0.640 0.533
12 Kazakhstan 0.297 0.589 0.000 0.722 1.000 0.231 0.256 0.249 0.497
Table 6. Pairwise comparison of relative PageRank scores citation impact of scientific publications from different subsets according to defined patterns.
Table 6. Pairwise comparison of relative PageRank scores citation impact of scientific publications from different subsets according to defined patterns.
No Country F ≺ M F f m ≺ M f m F m m ≺ M f f F f f ≺ M m m
1 USA 0.40763 0.22861 -0.02635 0.41552
2 China 0.21950 0.19224 -0.02604 0.31939
3 Germany 0.20118 0.21281 -0.07734 0.35907
4 France 0.39738 0.19106 0.21598 0.05431
5 Japan 0.07196 0.30275 0.16964 0.19786
6 G. Britain 0.16081 0.06608 -0.02529 0.04701
7 Italy 0.33818 0.14979 0.02018 0.35411
8 India 0.26352 0.17770 0.19945 0.31750
9 Canada 0.38451 0.15400 0.04888 0.15055
10 Spain 0.21723 0.18787 0.10011 0.27887
11 Ukraine 0.07203 0.38946 -0.30040 0.15423
12 Kazakhstan 0.74135 -0.00141 - 0.73906
Table 7. Pairwise comparison of relative PageRank scores citation impact of scientific publications from different research areas according to defined patterns.
Table 7. Pairwise comparison of relative PageRank scores citation impact of scientific publications from different research areas according to defined patterns.
No Research areas F ≺ M F f m ≺ M f m F m m ≺ M f f F f f ≺ M m m
1 Computer science 0,27503 0,24160 0,05931 0,37144
2 Artificial intelligence 0,24541 0,28160 0,09138 0,41557
3 Mathematics 0,17782 0,27730 0,03054 0,47048
4 Algorithm 0,31373 0,29600 0,15378 0,51274
5 Engineering 0,29077 0,21000 0,03516 0,33240
6 Computer vision 0,35407 0,26500 0,15399 0,38114
7 Computer network 0,22579 0,20350 0,14125 0,33291
8 Control theory 0,09866 0,25570 0,10023 0,26358
9 Pattern recognition 0,48960 0,35490 0,13328 0,51652
10 Mathematical optimization 0,34463 0,25860 0,14027 0,49677
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