Figure captions
Fig. 1. A simplified diagram of how intersectionality affects academic success, highlighting the barriers and solutions. (a) Academic success depends on training (T ), language (L ), networking (N ), and discrimination (D ). Here, T and Dare affected by several factors. T depends on the institution, economy of the country, and supervisor. Discrimination is mainly driven by whether an individual belongs to BIPOC (black, indigenous, or people of color), particular gender, LGBTQUIA+, has disabilities, and others (non-visual implicit biases based on name or institution). (b) The consideration of one dimension obscures the real variation among individuals. For instance, individuals with the same linguistic skills might have received different training in different countries, or individuals with the same training can be affected differently by discrimination.
Fig. 2. Intersectionality in academia across four axes: socioeconomy, language, networking, and discrimination. A. Maps showing the gross domestic product per capita of 2018 (GDP) (a), English fluency index (EF) (b), and visa-free score (number of countries that a country is allowed to travel to without a visa) (c) in 96 English as Foreign language countries. All three variables were log-transformed. B. Correlation between GDP, EF, and the visa-free score, highlighting three Global South countries (Nigeria, Tunisia, and Cameroon) and three Global North countries (Germany, Belgium, and Spain) (d-f). The black lines are linear regressions. Theoretical chart applying the equation of academic success [Success =f (Training[T ], Language[L ], Networking[N ], Discrimination[D ])] in random individuals from the six countries (g). The chart shows that because economic status is generally positively correlated with English proficiency and networking opportunities, researchers from the Global South have often at least three components (T, L , and N ) below average (-) while researchers from the Global North have typically these three components above average (+). The chart also illustrates the difference between microdiversity and macrodiversity, highlighting that within each country, researchers from different backgrounds (ethnicity or gender) could be affected by discrimination even if they are located in the Global North. Data on EF were obtained from (https://www.ef.com/ca/epi/). Data on GDP were obtained from WorldBank (https://data.worldbank.org/). Data on visa-free scores were obtained from Henley & Partners (https://www.henleyglobal.com/passport-index).