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