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).