<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec>
<h3>Purpose</h3>
<p>To contribute to the study of networks and graphs.</p>
</sec>
<sec>
<h3>Design/methodology/approach</h3>
<p>We apply standard mathematical thinking.</p>
</sec>
<sec>
<h3>Findings</h3>
<p>We show that the distance distribution in an undirected network Lorenz majorizes the one of a chain. As a consequence, the average and median distances in any such network are smaller than or equal to those of a chain.</p>
</sec>
<sec>
<h3>Research limitations</h3>
<p>We restricted our investigations to undirected, unweighted networks.</p>
</sec>
<sec>
<h3>Practical implications</h3>
<p>We are convinced that these results are useful in the study of small worlds and the so-called six degrees of separation property.</p>
</sec>
<sec>
<h3>Originality/value</h3>
<p>To the best of our knowledge our research contains new network results, especially those related to frequencies of distances.</p>
</sec>
</abstract>

Extended Lorenz majorization and frequencies of distances in an undirected network

<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec>
<h3>Purpose</h3>
<p>Accurately assigning the document type of review articles in citation index databases like Web of Science(WoS) and Scopus is important. This study aims to investigate the document type assignation of review articles in Web of Science, Scopus and Publisher’s websites on a large scale.</p>
</sec>
<sec>
<h3>Design/methodology/approach</h3>
<p>27,616 papers from 160 journals from 10 review journal series indexed in SCI are analyzed. The document types of these papers labeled on journals’ websites, and assigned by WoS and Scopus are retrieved and compared to determine the assigning accuracy and identify the possible reasons for wrongly assigning. For the document type labeled on the website, we further differentiate them into explicit review and implicit review based on whether the website directly indicates it is a review or not.</p>
</sec>
<sec>
<h3>Findings</h3>
<p>Overall, WoS and Scopus performed similarly, with an average precision of about 99% and recall of about 80%. However, there were some differences between WoS and Scopus across different journal series and within the same journal series. The assigning accuracy of WoS and Scopus for implicit reviews dropped significantly, especially for Scopus.</p>
</sec>
<sec>
<h3>Research limitations</h3>
<p>The document types we used as the gold standard were based on the journal websites’ labeling which were not manually validated one by one. We only studied the labeling performance for review articles published during 2017-2018 in review journals. Whether this conclusion can be extended to review articles published in non-review journals and most current situation is not very clear.</p>
</sec>
<sec>
<h3>Practical implications</h3>
<p>This study provides a reference for the accuracy of document type assigning of review articles in WoS and Scopus, and the identified pattern for assigning implicit reviews may be helpful to better labeling on websites, WoS and Scopus.</p>
</sec>
<sec>
<h3>Originality/value</h3>
<p>This study investigated the assigning accuracy of document type of reviews and identified the some patterns of wrong assignments.</p>
</sec>
</abstract>

An explorative study on document type assignment of review articles in Web of Science, Scopus and journals’ websites

<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec>
<h3>Purpose</h3>
<p>The purpose of this study is to develop and compare model choice strategies in context of logistic regression. Model choice means the choice of the covariates to be included in the model.</p>
</sec>
<sec>
<h3>Design/methodology/approach</h3>
<p>The study is based on Monte Carlo simulations. The methods are compared in terms of three measures of accuracy: specificity and two kinds of sensitivity. A loss function combining sensitivity and specificity is introduced and used for a final comparison.</p>
</sec>
<sec>
<h3>Findings</h3>
<p>The choice of method depends on how much the users emphasize sensitivity against specificity. It also depends on the sample size. For a typical logistic regression setting with a moderate sample size and a small to moderate effect size, either BIC, BICc or Lasso seems to be optimal.</p>
</sec>
<sec>
<h3>Research limitations</h3>
<p>Numerical simulations cannot cover the whole range of data-generating processes occurring with real-world data. Thus, more simulations are needed.</p>
</sec>
<sec>
<h3>Practical implications</h3>
<p>Researchers can refer to these results if they believe that their data-generating process is somewhat similar to some of the scenarios presented in this paper. Alternatively, they could run their own simulations and calculate the loss function.</p>
</sec>
<sec>
<h3>Originality/value</h3>
<p>This is a systematic comparison of model choice algorithms and heuristics in context of logistic regression. The distinction between two types of sensitivity and a comparison based on a loss function are methodological novelties.</p>
</sec>
</abstract>

A comparison of model choice strategies for logistic regression

<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec>
<h3>Purpose</h3>
<p>Interdisciplinary fields have become the driving force of modern science and a significant source of scientific innovation. However, there is still a paucity of analysis about the essential characteristics of disciplines’ cross-disciplinary impact.</p>
</sec>
<sec>
<h3>Design/methodology/approach</h3>
<p>In this study, we define cross-disciplinary impact on one discipline as its impact to other disciplines, and refer to a three-dimensional framework of variety-balance-disparity to characterize the structure of cross-disciplinary impact. The variety of cross-disciplinary impact of the discipline was defined as the proportion of the high cross-disciplinary impact publications, and the balance and disparity of cross-disciplinary impact were measured as well. To demonstrate the cross-disciplinary impact of the disciplines in science, we chose Microsoft Academic Graph (MAG) as the data source, and investigated the relationship between disciplines’ cross-disciplinary impact and their positions in the Hierarchy of Science (HOS).</p>
</sec>
<sec>
<h3>Findings</h3>
<p>Analytical results show that there is a significant correlation between the ranking of cross-disciplinary impact and the HOS structure, and that the discipline exerts a greater cross-disciplinary impact on its neighboring disciplines. Several bibliometric features that measure the hardness of a discipline, including the number of references, the number of cited disciplines, the citation distribution, and the Price index have a significant positive effect on the variety of cross-disciplinary impact. The number of references, the number of cited disciplines, and the citation distribution have significant positive and negative effects on balance and disparity, respectively. It is concluded that the less hard the discipline, the greater the cross-disciplinary impact, the higher balance and the lower disparity of cross-disciplinary impact.</p>
</sec>
<sec>
<h3>Research limitations</h3>
<p>In the empirical analysis of HOS, we only included five broad disciplines. This study also has some biases caused by the data source and applied regression models.</p>
</sec>
<sec>
<h3>Practical implications</h3>
<p>This study contributes to the formulation of discipline-specific policies and promotes the growth of interdisciplinary research, as well as offering fresh insights for predicting the cross-disciplinary impact of disciplines.</p>
</sec>
<sec>
<h3>Originality/value</h3>
<p>This study provides a new perspective to properly understand the mechanisms of cross-disciplinary impact and disciplinary integration.</p>
</sec>
</abstract>

Characterizing structure of cross-disciplinary impact of global disciplines: A perspective of the Hierarchy of Science

<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec>
<h3>Purpose</h3>
<p>The collaboration relationships between innovation actors at a geographic level may be considered as grouping two separate layers, the domestic and the foreign. At the level of each layer, the relationships and the actors involved constitute a Triple Helix game. The paper distinguished three levels of analysis: the global grouping together all actors, the domestic grouping together domestic actors, and the foreign related to only actors from partner countries.</p>
</sec>
<sec>
<h3>Design/methodology/approach</h3>
<p>Bibliographic records data from the Web of Science for South Korea and West Africa breakdown per innovation actors and distinguishing domestic and international collaboration are analyzed with game theory. The core, the Shapley value, and the nucleolus are computed at the three levels to measure the synergy between actors.</p>
</sec>
<sec>
<h3>Findings</h3>
<p>The synergy operates more in South Korea than in West Africa; the government is more present in West Africa than in South Korea; domestic actors create more synergy in South Korea, but foreign more in West Africa; South Korea can consume all the foreign synergy, which is not the case of West Africa.</p>
</sec>
<sec>
<h3>Research limitations</h3>
<p>Research data are limited to publication records; techniques and methods used may be extended to other research outputs.</p>
</sec>
<sec>
<h3>Practical implications</h3>
<p>West African governments should increase their investment in science, technology, and innovation to benefit more from the synergy their innovation actors contributed at the foreign level. However, the results of the current study may not be sufficient to prove that greater investment will yield benefits from foreign synergies.</p>
</sec>
<sec>
<h3>Originality/value</h3>
<p>This paper uses game theory to assess innovation systems by computing the contribution of foreign actors to knowledge production at an area level. It proposes an indicator to this end.</p>
</sec>
</abstract>

The Triple Helix of innovation as a double game involving domestic and foreign actors

<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec>
<h3>Purpose</h3>
<p>This paper aims to address the limitations in existing research on the evolution of knowledge flow networks by proposing a meso-level institutional field knowledge flow network evolution model (IKM). The purpose is to simulate the construction process of a knowledge flow network using knowledge organizations as units and to investigate its effectiveness in replicating institutional field knowledge flow networks.</p>
</sec>
<sec>
<h3>Design/Methodology/Approach</h3>
<p>The IKM model enhances the preferential attachment and growth observed in scale-free BA networks, while incorporating three adjustment parameters to simulate the selection of connection targets and the types of nodes involved in the network evolution process Using the PageRank algorithm to calculate the significance of nodes within the knowledge flow network. To compare its performance, the BA and DMS models are also employed for simulating the network. Pearson coefficient analysis is conducted on the simulated networks generated by the IKM, BA and DMS models, as well as on the actual network.</p>
</sec>
<sec>
<h3>Findings</h3>
<p>The research findings demonstrate that the IKM model outperforms the BA and DMS models in replicating the institutional field knowledge flow network. It provides comprehensive insights into the evolution mechanism of knowledge flow networks in the scientific research realm. The model also exhibits potential applicability to other knowledge networks that involve knowledge organizations as node units.</p>
</sec>
<sec>
<h3>Research Limitations</h3>
<p>This study has some limitations. Firstly, it primarily focuses on the evolution of knowledge flow networks within the field of physics, neglecting other fields. Additionally, the analysis is based on a specific set of data, which may limit the generalizability of the findings. Future research could address these limitations by exploring knowledge flow networks in diverse fields and utilizing broader datasets.</p>
</sec>
<sec>
<h3>Practical Implications</h3>
<p>The proposed IKM model offers practical implications for the construction and analysis of knowledge flow networks within institutions. It provides a valuable tool for understanding and managing knowledge exchange between knowledge organizations. The model can aid in optimizing knowledge flow and enhancing collaboration within organizations.</p>
</sec>
<sec>
<h3>Originality/value</h3>
<p>This research highlights the significance of meso-level studies in understanding knowledge organization and its impact on knowledge flow networks. The IKM model demonstrates its effectiveness in replicating institutional field knowledge flow networks and offers practical implications for knowledge management in institutions. Moreover, the model has the potential to be applied to other knowledge networks, which are formed by knowledge organizations as node units.</p>
</sec>
</abstract>

A new evolutional model for institutional field knowledge flow network

<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec>
<h3>Purpose</h3>
<p>This study aims to explore the geography of editors-in-chief to demonstrate which countries exercise the highest-level decision-making in scholarly communication. In addition, the study seeks to investigate the potential relationships between the origin and nationality of academic publishers and the geography of editors-in-chief.</p>
</sec>
<sec>
<h3>Design/methodology/approach</h3>
<p>The analysis involves 11,915 journals listed in Web of Science’s Social Sciences Citation Index (SSCI) and Science Citation Index Expanded (SCIE). These journals employ 15,795 scholars as editors-in-chief. The geographical locations of the institutions the editors-in-chief are affiliated with were identified; then, the data were aggregated at the country level.</p>
</sec>
<sec>
<h3>Findings</h3>
<p>The results show that most editors-in-chief are located in countries of the Anglosphere, primarily the United States and the United Kingdom. In addition, most academic publishers and professional organizations that publish academic journals were found to be based in the United States and the United Kingdom, where most editors-in-chief are also based.</p>
</sec>
<sec>
<h3>Research limitations</h3>
<p>The analysis involves journals indexed in the Web of Science’s SCIE/SSCI databases, which are demonstrably biased toward the English language. Furthermore, the study only takes a snapshot of the geography of editors-in-chief for the year 2022, but it does not investigate trends.</p>
</sec>
<sec>
<h3>Research implications</h3>
<p>The study maps the highest-level decision-making in scholarly communication.</p>
</sec>
<sec>
<h3>Originality/value</h3>
<p>The study explores and maps the geography of editors-in-chief by using a massive dataset.</p>
</sec>
</abstract>

Mapping the geography of editors-in-chief

AHEAD OF PRINT

Volume 9 (2024): Issue 1 (February 2024)

Volume 8 (2023): Issue 4 (November 2023)

Volume 8 (2023): Issue 3 (June 2023)

Volume 8 (2023): Issue 2 (April 2023)

Volume 8 (2023): Issue 1 (February 2023)

Volume 7 (2022): Issue 4 (November 2022)

Volume 7 (2022): Issue 3 (August 2022)

Volume 7 (2022): Issue 2 (April 2022)

Volume 7 (2022): Issue 1 (February 2022)

Volume 6 (2021): Issue 4 (November 2021)

Volume 6 (2021): Issue 3 (June 2021)

Volume 6 (2021): Issue 2 (April 2021)

Volume 6 (2021): Issue 1 (February 2021)

Volume 5 (2020): Issue 4 (November 2020)

Volume 5 (2020): Issue 3 (August 2020)

Volume 5 (2020): Issue 2 (April 2020)

Volume 5 (2020): Issue 1 (February 2020)

Volume 4 (2019): Issue 4 (December 2019)

Volume 4 (2019): Issue 3 (August 2019)

Volume 4 (2019): Issue 2 (May 2019)

Volume 4 (2019): Issue 1 (February 2019)

Volume 3 (2018): Issue 4 (November 2018)

Volume 3 (2018): Issue 3 (August 2018)

Volume 3 (2018): Issue 2 (May 2018)

Volume 3 (2018): Issue 1 (February 2018)

Volume 2 (2017): Issue 4 (December 2017)

Volume 2 (2017): Issue 3 (August 2017)

Volume 2 (2017): Issue 2 (May 2017)

Volume 2 (2017): Issue 1 (February 2017)

Volume 1 (2016): Issue 4 (November 2016)

Volume 1 (2016): Issue 3 (August 2016)

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Volume 1 (2016): Issue 1 (February 2016)

Journal of Data and Information Science

Journal of Data and Information Science (JDIS, formerly Chinese Journal of Library and Information Science), sponsored by the Chinese Academy of Sciences (CAS) and published quarterly by the National Science Library of CAS, is the first internationally published English-language academic journal in Library and Information Science and related fields from China.  The Journal of Data and Information Science (JDIS) focuses on data-based research oriented toward the exploration of scientific research and innovation. The main areas of interest are science of science, evidence-based policymaking, research evaluation, computational social science, and scientometrics/bibliometrics/altmetrics/ informetrics. Emphasis is given to research that focuses on data, analytics, and knowledge discovery, and supports decision making and science policy. This includes modeling, innovation, data security, media and communications, and social development. Topics may include studies of metadata or full content data, text or non-textural data, structured or non-structural data, domain-specific or cross-domain data, and dynamic or interactive data.  Specific topic areas may include (but are not limited to):    Knowledge organization  Knowledge discovery and data mining  Knowledge integration and fusion  Semantic Web  Science of science  Bibliometrics and scientometrics  Analytic and diagnostic informetrics  Competitive intelligence  Predictive analysis  Social network analysis and metrics  Semantic and interactively analytic retrieval  Evidence-based policy analysis  Intelligent knowledge production  Knowledge-driven workflow management and decision-making  Knowledge-driven collaboration and its management  Domain knowledge infrastructure with knowledge fusion and analytics  Training for data &amp; information scientists  Development of data and information services    JDIS publishes theoretical and empirical work. Systematic reviews are welcome and applied research in development of advanced methods, services, and best practices is also an important part. But simple application of established informetrics on a specific research field or country is out of the scope.Welcome to submit your papers to JDIS.  Why subscribe and read  JDIS is the first and only English journal from China in Library and Information Science and related fields. With an aim to disseminate the cutting-edge research in these fields, it is devoted to the study and application of the theories, methods, techniques, services, and infrastructural facilities using big data to support knowledge discovery for decision and policy making. The basic emphasis is big data-based, analytics centered, knowledge discovery driven, and decision making supporting. JDIS has gathered a big body of high profile experts across the world who contribute their research to the journal. The international authors account for around 62% in its first publication year (2016).  Why submit  JDIS is the first and only English journal from China in Library and Information Science and related fields. It owns a number of world front-line scholars as editorial board members or reviewers. The turnaround time on average for a manuscript from submission to final decision is less than two and a half months.  Archiving  Sciendo archives the contents of this journal in Portico- digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 