A theory of justice applied to academic institutions
Based on Rawls’ theory of justice, I propose a novel framework that could be implemented in practice to integrate the ecology of human development into the process of distribution of goods by academic institutions. In this framework, collecting more data from the ecological conditions experienced by individuals can in fact be used to compensate for ecological factors known to influence academic achievements. This is perhaps counterintuitive given the concept of the veil of ignorance which aims to remove information from individuals thereby allowing for a fair agreement of principles. This is perhaps even more paradoxical given the idea that we shoulddecrease (rather than increase) the amount of data collected from individuals in order to generate fair judgements. Yet, I argue that collecting more information is the only way in which academic institutions can compensate for the impossibility to access the original position. This is because documenting known ecological factors that have influenced equality of opportunity in the immediate and broader (e.g., country, cultural values) context of an individual is the only course of action that allows for an expectation of academic achievement relative to opportunity to be built. In other words, academic institutions cannot make fair judgements unless the information about the causes of unfairness is known. Such system require that the academic institutions collect data on, for example, ethnicity, average income, country of education, native language and so on for every applicant, which is then processed using algorithms to minimise human biases [although in the present time, algorithms too have biases (Noble, 2018) which further research should eliminate in the future]. Only with this pool of information can the academic institutions standardise and compare the academic opportunities and merit of applicants given the ecological context that different applicants developed. The algorithm uses data from the same ecological context of a given individual to generate an expected prediction of academic performance from which a standardised score of the applicant relative to the ecological context can be generated (‘ecological score’ of the applicant, Fig 1). In parallel, an anonymised version of the project is then peer-reviewed and scored using traditional peer-review process for methodological merit (‘peer-review score’, Fig 1). Both scores are then combined to create a standardized score for the application (‘total standardized score’, Fig 1), which can then be used for decision-making. The final score represents a fair process through which academic institutions respect and comply to the concept of equality of opportunity (or