Academia is far from a meritocratic distribution of opportunities. This leads to inequalities, lack of diversity, and unfairness. The objective of this conceptual paper is to propose an integrative framework to help the academic community address the pervasive but persistent inequalities of opportunities. The framework emerges from the intersections of Bourdieu, Bronfenbrenner, and Rawls frameworks and propose the use of ethical Artificial Intelligence (AI) to contextualise merit and generate true equality of opportunities. More specifically, I argue that academia has structures and doxa that may be inaccessible to individuals from different social origins, and perpetuated by those privileged individuals who achieve positions of power within academia.  The privileged individuals inherit and are exposed to opportunities to acquire capital from early life, resulting in the continuation of status quo practices and alienation of minorities that do not share – or do not have the ability to acquire – capital. I argue that this process occurs from as a result of the social origins of the individual and that Bronfenbrennian framework suggests that not only disadvantaged individuals lack (inherited) capital, but also lack the ability and opportunities to acquire capital relative to privileged counterparts. I argue that the only way to mitigate this inequitable system is to retrieve the Rawlsian original position of ignorance (veil of ignorance) in the allocation of academic capital based on merit, which can only be objectively quantified relative to social origins of individuals. As opposed to current subjective assessments (e.g., peer-review) or lottery systems, I propose the use of Big Data and ethical AI to reconstruct the position of ignorance and contextualise merit based on the expected merit given individuals’ social origins. I also discuss the concept of ‘years post-PhD’ as it is used to introduce fairness in allocation of academic capital, and propose a different and less relativistic landmark that accounts for the years post-first authorship publication. This is a novel conceptual framework which can stimulate further research into the ecology of social justice.

The molecular characterisation of complex behaviours is a challenging task as a range of different factors are often involved to produce the observed phenotype. An established approach is to look at the overall levels of expression of brain genes – known as ‘neurogenomics’ – to select the best candidates that associate with patterns of interest. This approach has relied so far on a set of powerful statistical tools capable to provide a snapshot of the expression of many thousands of genes that are present in an organism’s genome. However, traditional neurogenomic analyses have some well-known limitations; above all, the limited number of biological replicates compared to the number of genes tested – often referred to as “curse of dimensionality”. Here we implemented a new Machine Learning (ML) approach that can be used as a complement to established methods of transcriptomic analyses. We tested three types of ML models for their performance in the identification of genes associated with honeybee waggle dance. We then intersected the results of these analyses with traditional outputs of differential gene expression analyses and identified two promising candidates for the neural regulation of the waggle dance: the G-protein coupled receptor boss and hnRNP A1, a gene involved in alternative splicing. Overall, our study demonstrates the application of Machine Learning to analyse transcriptomics data and identify genes underlying social behaviour. This approach has great potential for application to a wide range of different scenarios in evolutionary ecology, when investigating the genomic basis for complex phenotypic traits.

Authors: Natalie E. Duffus1, Juliano Morimoto1,2*Authors’ Affiliations:School of Biological Sciences, University of Aberdeen, Zoology Building, Tillydrone Ave, Aberdeen AB24 2TZPrograma de Pós-graduação em Ecologia e Conservação, Universidade Federal do Paraná, Curitiba, 82590-300, Brazil*Correspondence: [email protected]’ contributions. ND and JM designed the study. ND collected and analysed the data and wrote the first draft of the manuscript. Both authors contributed to the writing of the final version of the manuscript and approved its submission.Declaration of interest: We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.Funding Statement: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.