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

Abstract

SignificanceContemporary social sciences aim to be diverse and inclusive, but traces of the historical dominance of Western European and North American academic institutions persist in scientific practices. One such practice is the phrasing of article titles. Our analysis shows that articles studying the global North are systematically less likely to mention the name of the country they study in their title compared to articles on the global South. This constitutes, potentially, an unwarranted claim on universality and may lead to lesser recognition of global South studies. Social and behavioral scientists must reflect on the phrasing of their article titles to avoid reproducing harmful relations of intellectual domination which limit inclusivity and constitute a barrier to the generalizability of scientific knowledge.

Keywords: Eurocentrism; global inequalities; knowledge production.

Fig. 1.

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). Note that the regional classification of countries is taken from the UN Sustainable Development Goals (https://unstats.un.org/sdgs/indicators/regional-groups/). We merge the category “Australia and New Zealand” into one category, “Europe and Northern America,” and excluded papers about Oceania because of the small sample size (n = 1,583).

Fig. 2.

Global disparities in knowledge production and papers’ localization. (Top) Five-group classification of the total number of mentions of country names and demonyms in the analytical sample. (Bottom) Five-group classification of countries based on the localization rate of studies that mention a country name or demonym in their abstract. For both panels, the five categories are obtained by applying Jenk’s algorithm to the country-level number of mentions and localization rates, respectively. Jenk’s algorithm minimizes the variance within categories and maximizes the variance between categories.

Fig. 3.

Regression coefficients for a multivariate Poisson model predicting the localization (Y = 1) of papers. The rectangles comprise the 95% CI. CI are obtained by adding and subtracting the corresponding SE of each coefficient multiplied by the 97.5th percentile of a standard normal distribution. The category “Europe and North America” includes Australia and New Zealand. The deviance of this specification is 342,443, which is substantially lower than the degrees of freedom (i.e., 547,784), suggesting that the model fits the data adequately.

Fig. 4.

(Left) Regression coefficients for the top 10 most studied countries and other countries, grouped by regions. (Right) Regression coefficients for the most studied country within region and other countries, grouped by region. Single-country coefficients are plotted with a darker shade than countries grouped in regions. Rectangles comprise the 95% CI. CI are obtained by adding and subtracting the corresponding SE of each coefficient multiplied by the 97.5th percentile of a standard normal distribution. The category “Europe and North America” includes Australia and New Zealand. The deviances for model specifications in A and B (332,926 and 333,182, respectively) are substantially lower than the degrees of freedom, indicating an adequate fit.