Laboratory

Population Dynamics and Sustainable Well-Being

Project

Trends and Disparities in Patterns of Scientific Knowledge Production

Aliakbar Akbaritabar, Diego Alburez-Gutierrez, Andres Castro (MPIDR / Autonomous University of Barcelona, Centre for Demographic Studies, Spain); in Collaboration with Vincent Larivière (University of Montreal, Canada), Cassidy R. Sugimoto (Georgia Institute of Technology, Atlanta, USA), Flaminio Squazzoni (Università degli Studi di Milano, Italy), Dimity M. Stephen (German Centre for Higher Education Research and Science Studies GmbH, Hannover, Germany), Dehua Gao (Shandong Technology and Business University, Yantai City, China)

Detailed Description

The digitization of data across different contexts has led to the increased accumulation and gathering of science and scientific metadata that have been released through proprietary and licensed databases. Bibliometric data, i.e., information extracted from scientific publications, can be considered a source of digital trace data of scientists’ behaviors. It is thus possible to use this data to study inequalities in patterns of scientific production worldwide.

A number of scientific fields leverage bibliometric data, including the science of science, scientometrics, and the sociology of science. These fields deal with a diverse set of research questions aimed at understanding how scientists do science, who collaborates with whom, and what kind of teams develop and produce more scientific breakthroughs than others. In these contexts, bibliometric data are used, for example, to trace scientists’ behaviors, investigate science systems, or assess gender disparities in academia.

The use of bibliometric data in demographic research can be roughly divided into two classes. One class of studies looks at the populations of scholars and uses bibliometric data to study their composition and dynamics across different national science systems and over time, including disparities by gender or academic age. The second class repurposes bibliometric data and treats it similarly to digital traces to study, for example, scholars’ mobility and migration. These data are often combined with other sources such as geo-spatial data, surveys, and administrative data to provide more context to the scientists’ behaviors.

In this project, we study global trends in scientific knowledge production in the form of scientific publications. We investigate the structure of scientific collaboration worldwide and how this collaboration varies in different national contexts and among fields of science. We also investigate the behaviors of scientists along the following questions: (i) How do scientists select research questions and analysis frameworks? (ii) With whom do they collaborate? (iii) How do they react to national or global incentives in research assessment exercises? (iv) Do they have specific tendencies in how they present their work depending on the research focus being on the Global North or Global South?

In one study, we used Scopus data from 1996 to 2020 to understand whether publications have a different tendency in mentioning the regions they focus on. We have found that publications studying the Global North have a higher tendency not to mention the region, whereas studies on the Global South did mention the region much more often.

In a related study, we focused on the rate of internationalized scientific collaborations worldwide in studies produced by scholars in higher education. We investigated whether these scholars, located in different geographic regions, had different tendencies for international collaboration. We have found a highly internationalized structure of collaboration in Europe, Asia, Africa, and Oceania, and a locally structured collaboration structure in the Americas.

Naming practices in mentioning the geographic location of focus in the title of scientific publications

While about 60% of articles have a focus on Europe and North America (left) only 40% of articles mention these regions in their title (right). The reverse is the case for other regions of the world. The figure shows the distribution of the region of focus of articles in the analytical sample (Left) and localization rate by region of study (Right), 1996 to 2020 (n = 560,893). Authors from the Global South have a much higher tendency to reveal the geographic focus of their articles. © Based on the paper by Castro Torres, A. F., & Alburez-Gutierrez, D. (2022). North and South: Naming practices and the hidden dimension of global disparities in knowledge production. Proceedings of the National Academy of Sciences, 119(10), e2119373119

The regional concentration of science and scientific collaborations

While institutions in Americas are collaborating mainly with other institutions inside the same region, Europe, Africa, and Oceania are collaborating with each other and show a much higher trend of internationalized collaboration in the area of Higher Education studies. The figure shows the distribution of Higher Education (HE) studies institutions from different continents (left column of both panels) versus their membership in 36 HE studies network. Right column of both panels show the cohesive subgroups (i.e., communities) that we have identified their collaboration network. Co-authoring of publications is converted into a network where authors form one institution collaborate with authors from another institution (the left panel shows size of communities based on number of institutions, the right panel shows the productivity based on number of publications). © Based on the paper by Akbaritabar, A., & Barbato, G. An internationalised Europe and regionally focused Americas: A network analysis of higher education studies. European Journal of Education, 56(2), 219–234.

Research Keywords:

Culture, Education and Science, Projections and Forecasting, Statistics and Mathematics

Region keywords:

Europe, Germany, Italy, World

Links:

How to Use Bibliometric Data for Demographic Research

Where Social Science meets Computational Science

Publications

Akbaritabar, A.:

Thinking spatially in computational social science: commentary on Yong-Yeol Ahn (2021): “Representation learning for computational imagination”

EPJ Data Science 13:14, 1–12. (2024)

Basellini, U.:

Open science practices in demographic research: an appraisal

Demographic Research 50:43, 1265–1280. (2024)

Castro Torres, A. F.; Akbaritabar, A.:

The use of linear models in quantitative research

Quantitative Science Studies, 1–32. (2024)

Abdi Nur, A.; Akbaritabar, A.; Vignau Loria, M.; Coolman, N.; Fan, X.; Curran, S.:

Bibliometric analysis of published literature on the determinants of family planning

MPIDR Working Paper WP-2023-027. (2023)

Akbaritabar, A.:

Scientific collaboration networks in the interdisciplinary field of biodiversity in central Germany

In: 19th International Conference on Scientometrics and Informetrics, ISSI 2023: 2-5 July 2023, Indiana University, Bloomington, IN, USA; proceedings: volume 1, 19–41. Bloomington, IN: Indiana University. (2023)

Akbaritabar, A.; Castro Torres, A. F.; Larivière, V.:

A global perspective on the social structure of science

MPIDR Working Paper WP-2023-029. (2023)

Akbaritabar, A.; Stephen, D.; Squazzoni, F.:

A study of referencing changes in preprint-publication pairs across multiple fields

Journal of Informetrics 16:2, 101258–101258. (2022)

Castro Torres, A. F.; Akbaritabar, A.:

Methodological monotheism across fields of science in contemporary quantitative research

arXiv e-prints 2208.05373. unpublished. (2022)

Castro Torres, A. F.; Alburez-Gutierrez, D.:

North and South: naming practices and the hidden dimension of global disparities in knowledge production

Proceedings of the National Academy of Sciences of the United States of America 119:10, e2119373119–e2119373119. (2022)