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Deductive and theory-driven research starts by asking questions. Finding tentative answers to these questions in the literature is next. It is followed by gathering, preparing and modelling relevant data to empirically test these tentative answers. Inductive research, on the other hand, starts with data representation and finding general patterns in data. Ahn suggested, in his keynote speech at the seventh International Conference on Computational Social Science (IC2S2) 2021, that the way this data is represented could shape our understanding and the type of answers we find for the questions. He discussed that specific representation learning approaches enable a meaningful embedding space and could allow spatial thinking and broaden computational imagination. In this commentary, I summarize Ahn’s keynote and related publications, provide an overview of the use of spatial metaphor in sociology, discuss how such representation learning can help both inductive and deductive research, propose future avenues of research that could benefit from spatial thinking, and pose some still open questions.

Background A previous meta-analysis (Kuiper et al., 2016) has shown that multiple aspects of social relationships are associated with cognitive decline in older adults. Yet, results indicated possible bias in estimations of statistical effects due to the heterogeneity of study design and measurements. We have updated this meta-analysis adding all relevant publications from 2012 to 2020 and performed a cumulative meta-analysis to map the evolution of this growing field of research (+80% of studies from 2012-2020 compared to the period considered in the previous meta-analysis). Methods Scopus and Web of Science were searched for longitudinal cohort studies examining structural, functional and combined effects of social relationships. We combined Odds Ratios (OR) with 95% confidence intervals (CI) using random effects meta-analysis and assessed sources of heterogeneity and the likelihood of publication bias. The risk of bias was evaluated with the Quality of Prognosis Studies in Systematic Reviews (QUIPS) tool. Results The review was prospectively registered on PROSPERO (ID: CRD42019130667). We identified 34 new articles published in 2012-2020. Poor social relationships were associated with cognitive decline with increasing precision of estimates compared to previously reviewed studies [(for structural, 17 articles, OR: 1.11; 95% CI: 1.08; 1.14) (for functional, 16 articles, OR: 1.12; 95% CI: 1.05; 1.20) (for combined, 5 articles, OR: 1.15; 95% CI: 1.06; 1.24)]. Meta-regression, risk and subgroup analyses showed that the precision of estimations improved in recent studies mostly due to increased sample sizes. Conclusions Our cumulative meta-analysis would confirm that multiple aspects of social relationships are associated with cognitive decline. Yet, there is still evidence of publication bias and relevant information on study design is often missing, which could lead to an over-estimation of their statistical effects.

This paper examines the structure of scientific collaborations in Berlin as a specific case with a unique history of division and reunification. It aims to identify strategic organizational coalitions in a context with high sectoral diversity. We use publications data with at least one organization located in Berlin from 1996–2017 and their collaborators worldwide. We further investigate four members of the Berlin University Alliance (BUA), as a formerly established coalition in the region, through their self-represented research profiles compared with empirical results. Using a bipartite network modeling framework, we move beyond the uncontested trend towards team science and increasing internationalization. Our results show that BUA members shape the structure of scientific collaborations in the region. However, they are not collaborating cohesively in all fields and there are many smaller scientific actors involved in more internationalized collaborations in the region. Larger divides exist in some fields. Only Medical and Health Sciences have cohesive intraregional collaborations, which signals the success of the regional cooperation established in 2003. We explain possible underlying factors shaping the intraregional groupings and potential implications for regions worldwide. A major methodological contribution of this paper is evaluating the coverage and accuracy of different organization name disambiguation techniques.

This article examines publication patterns over the last seventy years from the American Sociological Review and American Journal of Sociology, the two most prominent journals in sociology. We reconstructed the gender of all published authors and each author’s academic pedigree. Results would suggest that these journals published disproportionally more articles by male authors and their coauthors. These gender inequalities persisted even when considering citations and after controlling for the influence of academic affiliation. It would seem that the potentially positive advantage of working in a prestigious, elite sociology department, in terms of better learning environment and reputational signal, for higher publication opportunities only significantly benefits male authors. While our findings do not mean that these journals have biased internal policies or implicit practices, this publication pattern needs to be considered especially regarding the possibility of their “social closure” and isomorphism.

A lack of comprehensive migration data is a major barrier for understanding the causes and consequences of migration processes, including for specific groups like high-skilled migrants. We leverage large-scale bibliometric data from Scopus and OpenAlex to trace the global movements of scholars. Based on our empirical validations, we develop pre-processing steps and offer best practices for the measurement and identification of migration events. We have prepared a publicly accessible dataset that shows a high level of correlation between the counts of scholars in Scopus and OpenAlex for most countries. Although OpenAlex has more extensive coverage of non-Western countries, the highest correlations with Scopus are observed in Western countries. We share aggregated yearly estimates of international migration rates and of bilateral flows for 210 countries and areas worldwide for the period 1998–2020 and describe the data structure and usage notes. We expect that the publicly shared dataset will enable researchers to further study the causes and the consequences of migration of scholars to forecast the future mobility of academic talent worldwide.

This paper presents an overview of all scientific contributions using agent-based modeling (ABM) methodology in routine dynamics research. That is a specialized area of study and our extensive literature search revealed only a total of 12 contributions. We did a quantitative analysis of these published literature using co-authorship, cross-citation and bibliographic coupling methods. We then complemented the overview with a content analysis of substantial focus of these literatures. We summarized their findings and showed how ABM is applied in routine dynamics research. By elaborating on what has been done, we provide an overview of this newly emerging research field and present possible directions for future work. Even though there are only a few publications in this research field, we expect that dialogue between ABM modelers and routine scholars would be beneficial to promote the development in this area. It would be conducive to advancing the understanding of complex internal structure, processes and dynamics of organizational routines.

The diversity of analysis frameworks used in different fields of quantitative research is understudied. Using bibliometric data from the Web of Science (WoS), we conduct a large-scale and cross-disciplinary assessment of the proportion of articles that use linear models in comparison to other analysis frameworks from 1990 to 2022 and investigate the spatial and citation patterns. We found that, in absolute terms, linear models are widely used across all fields of science. In relative terms, three patterns suggest that linear-model-based research is a dominant analysis framework in Social Sciences. First, almost two-thirds of research articles reporting a statistical analysis framework reported linear models. Second, research articles from underrepresented countries in the WoS data displayed the highest proportions of articles reporting linear models. Third, there was a citation premium to articles reporting linear models in terms of being cited at least once for the entire period, and for the average number of citations until 2012. The confluence of these patterns may not be beneficial to the Social Sciences, as it could marginalize theories incompatible with the linear models’ framework. Our results have implications for quantitative research practices, including teaching and education of the next generations of scholars.

To study stratification among scientists, we reconstruct the career-long trajectories of 8.2 million scientists worldwide using 12 bibliometric measures of productivity, geographical mobility, collaboration, and research impact. While most previous studies examined these variables in isolation, we study their relationships using Multiple Correspondence and Cluster Analysis. We group authors according to their bibliometric performance and academic age across six macro fields of science, and analyze co-authorship networks and detect collaboration communities of different sizes. We found a stratified structure in terms of academic age and bibliometric classes, with a small top class and large middle and bottom classes in all collaboration communities. Results are robust to community detection algorithms used and do not depend on authors’ gender. These results imply that increased productivity, impact, and collaboration are driven by a relatively small group that accounts for a large share of academic outputs, i.e., the top class. Mobility indicators are the only exception with bottom classes contributing similar or larger shares. We also show that those at the top succeed by collaborating with various authors from other classes and age groups. Nevertheless, they are benefiting disproportionately from these collaborations which may have implications for persisting stratification in academia.

Research shows that global knowledge production is unequal, leading to disparities in understanding across topics, geographies, and populations. This inequality is particularly concerning for critical social and political issues like immigration, one of the most divisive topics worldwide. Using Scopus bibliometric data from 1996 to 2022, we compile a comprehensive corpus of about 125,000 migration-related publications spanning all academic disciplines and interdisciplinary fields. By analyzing their content, we identify gaps, measure missed opportunities, and examine the demographic over- and under-representation of countries. Our findings reveal that some countries and subregions remain systematically underrepresented despite having significant migrant populations. Key factors influencing this underrepresentation include research investment and geographic location, with African countries, Central Asia, and Latin America and the Caribbean being particularly affected. Moreover, emigration (i.e., accumulated outflow) is more strongly linked to a country’s research visibility than immigration (i.e., accumulated inflow). These findings have broad implications for resource allocation in migration research, its societal relevance, and the need for more evidence-based policy debates on migration.

Deductive and theory-driven research starts by asking questions. Finding tentative answers to these questions in the literature is next. It is followed by gathering, preparing and modelling relevant data to empirically test these tentative answers. Inductive research, on the other hand, starts with data representation and finding general patterns in data. Ahn suggested, in his keynote speech at the seventh International Conference on Computational Social Science (IC 2 S 2 ) 2021, that the way this data is represented could shape our understanding and the type of answers we find for the questions. He discussed that specific representation learning approaches enable a meaningful embedding space and could allow spatial thinking and broaden computational imagination. In this commentary, I summarize Ahn’s keynote and related publications, provide an overview of the use of spatial metaphor in sociology, discuss how such representation learning can help both inductive and deductive research, propose future avenues of research that could benefit from spatial thinking, and pose some still open questions.