Using openly accessible surname-publication data to infer diversity and meritocracy in academia

Quantifying institutional level diversity and meritocracy is generally unfeasible because it requires large research efforts (such as surveys and controlled hiring experiments) that are not in the interest of the existing power structure. We circumvent this conundrum by proposing the use of openly accessible surname-publication data for quantifying intergenerational representation in academia, which captures socioeconomic aspects of diversity. We then use a stochastic individual-based model of the intergenerational cycle of academic selection and reproduction to link representation with merit in academia. We distinguish merit, or the potential to produce future academic goods if everyone were given the same opportunities, from capital, or produced academic goods (including past publications, social networks, and grants) that may or may not predict merit. Comparing data from US academic and income groups with model predictions suggests that US academia perpetuates socioeconomic inequalities and underperforms compared to a more diverse academia. For academic ecologists aiming to tackle global crises, a lack of diversity and merit may ultimately prevent collective actions necessary to avert crises. This study reveals the magnitude of inequality in academia, supports affirmative action, and calls for a revamping of recruitment criteria that distinguish merit from capital.