Charles Jonathan Gomez

Interests

Research

I study the rising inequality in global scientific knowledge production and diffusion. I use topic models, social network analysis, and simulations and I focus on hierarchies, diversity, diffusion, and novelty in my work.

Courses

Scholarly Contributions

Journals/Publications

• Gomez, C., Lieberman, D., & Mäkinen, E. (2024). Hedgehogs, foxes, and global science ecosystems: Decoding universities' research profiles across fields with nested ecological networks. Research Policy, 53(7). doi:10.1016/j.respol.2024.105040
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  Modern scientific research evokes ecological imagery and metaphors, given that it is global, interdependent, and diverse. Ecological network structures—like matrices of species inhabiting islands across an archipelago—can be reordered to form nested patterns. These patterns describe the overall health of ecosystems, place species on a spectrum between being described as generalists (foxes) or specialists (hedgehogs), and which of these interactions might appear or disappear. Using the number of citations universities receive for work published in a particular subfield taken from over 66 million scientific publications in OpenAlex, we construct and analyze yearly nested ecological networks of a dozen academic fields between 1990 and 2017. We find increasingly nested structures across fields infer future acknowledgment in different subfields. We argue that this framework can inform policy on scientific research and university funding and evaluation.
• Gomez, C. J., Herman, A. C., & Parigi, P. (2022). Leading countries in global science increasingly receive more citations than other countries doing similar research. Nature Human Behaviour, 6(7), 919--929.
• Vesselinov, E., Villamizar-Santamaría, S. F., Gomez, C. J., & Fernández, E. M. (2019). A global community or a global waste of time? Content analysis of the Facebook site “Humans of New York”. Journal of Urban Affairs, 43(1), 117--139.
• Gomez, C. J., Herman, A. C., & Parigi, P. (2020). Moving more, but closer: Mapping the growing regionalization of global scientific mobility using ORCID. Journal of Informetrics, 14(3), 101044.
• Alperin, J. P., Gomez, C. J., & Haustein, S. (2019). Identifying diffusion patterns of research articles on Twitter: A case study of online engagement with open access articles. Public Understanding of Science, 28(1), 2--18.
• Gomez, C. J., & Lazer, D. (2019). Clustering knowledge and dispersing abilities enhances collective problem solving in a network. Nature Communications, 10(1), 1--11.
• Horowitz, A., Gomez, C. J., & others, . (2018). Identity override: How sexual orientation reduces the rigidity of racial boundaries. Sociological Science, 5, 669--693.
• Evans, E. D., Gomez, C. J., & McFarland, D. A. (2016). Measuring Paradigmaticness of Disciplines Using Text. Sociological Science, 3, 757--778.
• Gomez, C. J., & Parigi, P. (2015). The regionalization of intergovernmental organization networks: A non-linear process. Social Networks, 43, 192--203.
• Kizilcec, R. F., Bailenson, J. N., & Gomez, C. J. (2015). The instructor’s face in video instruction: Evidence from two large-scale field studies.. Journal of Educational Psychology, 107(3), 724.
• Bustos, S., Gomez, C., Hausmann, R., & Hidalgo, C. A. (2012). The dynamics of nestedness predicts the evolution of industrial ecosystems. PloS One, 7(11), e49393.

Presentations

• Leahey, E. E., Nanoti, A., Langalia, M., Lee, J., Gomez, C. J., & Bratt, S. E. (2023, June). Division of Labor in Data-Intensive Science: Implications for Innovation and Equity. 2nd International Conference of Science of Science & Innovation (ICSSI). Kellogg Global HUB, Northwestern University, Evanston, IL, USA: Digital Science.
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  In this paper, we systematically analyze the international division of labor on 1.2 million datasets submitted to GenBank over 29 years (1992-2021). GenBank [1] is an international open research data repository for the genomics community hosted by NCBI – and through which the Human Genome Project was conducted and COVID-19 sequences submitted – mak- ing it an ideal site to analyze the global distribution of labor on datasets. To classify countries, we use the the World Bank Income Classification [2] and a newer measure, the Scientific and Technical Capacity Index (STCI) [7], nuancing the binary of N-S. We analyzed the yearly struc- tures and dynamics of the division of N-S division of labor on genomic datasets by calculating the ratio of overlap of scientists appearing as (co)contributors to the dataset and on the dataset’s associated publication(s), inferring that a higher overlap is indicative of “coreness” in flat teams [8]. Coreness is indicative that the dataset submitter is more ‘core’ to the project, indicating the technical labor on a project is drawn into the intellectual center of the study. We find: (1) Scientists from the global south tend to be listed as datasets contributors more often that of global north researchers. Overlap increases overall, but there remain dis- tinct functional roles; that is, 40 percent of scientists are only dataset contributors. This finding is surprising given prior studies reporting the lack of infrastructures to produce and curate data in low income or scientifically developing countries. However, it could be that contribution is explained by the high frequency of N-S collaborations in genomics research on infectious diseases [5], leading to southern scientists being equipped to collect and submit datasets. (2) We identify a positive relationship between the “flatness” of a team and southern scientists leading or last author on the publication.

Poster Presentations

• Leahey, E. E., Lee, J., Langalia, M., Devitt, W., Gomez, C. J., & Bratt, S. E. (2023, June). North-South Collaborations on Scientific Datasets: A Longitudinal Exploration (1992-2021). 2nd International Conference of Science of Science & Innovation. Kellogg Global HUB, Northwestern University, Evanston, IL, USA: Digital Science.
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  In this paper, we systematically analyze the frequency of N-S collaborations on approx- imately 1.2 million sequences submitted to GenBank over 29 years (1992-2021). GenBank [2] is an international open research data repository for the genomics community hosted by NCBI, and in which the Human Genome Project sequences were shared and infectious disease sequences submitted (including COVID-19) making GenBank an ideal site to analyze N-S col- laborations on datasets. To classify countries we use the World Bank Income Classification [4] and the Scientific and Technical Capacity Index (STCI) [11]. We find: (1) datasets are disproportionately produced by the global north, but there is a higher rate of collaborations between nations with discrepant S&T capacity on datasets over time. The preponderance of the datasets submitted are domestic collaborations, but where there is international collaborations, over 89 percent are collaborations among scientifically advanced countries. The N-S collaborations networks demonstrate “burstiness” in their forma- tion and dissolution [5], suggesting scientific reactivity to outbreaks of infectious disease (e.g. HIV/AIDs) and ad hoc influx of resources to build capacity in southern scientists’ institutions (see Figure 1). (2) The classification indices commonly used to characterize the global north and south at a national level are incompatible revealing a need for composite mea- sures to nuance the N-S binary. The S&T capacity index [11] to the need for measures that capture the multi-faceted nature of the N-S political economy [1, 7], where S&T capacity and income measures are not interchangeable. For instance, United Arab Emirates is classified as a High Income Country (HIC) by the World Bank income classification, but as a Scientifically Lagging Country (SLC) by the parameters of the S&T index.