Topics and institutions in the reproduction of intersectional inequalities in science

Doctoral thesis (2023)

Race and gender homophily in collaborations and citations

Scientific Conference (2022, October 09)

Automatic Classification of Peer Review Recommendation

Poster (2022, September 08)

Desigualdades interseccionales en la ciencia

Speeches/Talks (2022)

Intersectional inequalities in science

Scientific Conference (2022, April 28)

Avoiding bias when inferring race using name-based approaches

in PLoS ONE (2022), 3(17), 0264270

Racial disparity in academia is a widely acknowledged problem. The quantitative understanding of racial-based systemic inequalities is an important step towards a more equitable research system. However ... [more ▼]

Racial disparity in academia is a widely acknowledged problem. The quantitative understanding of racial-based systemic inequalities is an important step towards a more equitable research system. However, because of the lack of robust information on authors’ race, few large-scale analyses have been performed on this topic. Algorithmic approaches offer one solution, using known information about authors, such as their names, to infer their perceived race. As with any other algorithm, the process of racial inference can generate biases if it is not carefully considered. The goal of this article is to assess the extent to which algorithmic bias is introduced using different approaches for name-based racial inference. We use information from the U.S. Census and mortgage applications to infer the race of U.S. affiliated authors in the Web of Science. We estimate the effects of using given and family names, thresholds or continuous distributions, and imputation. Our results demonstrate that the validity of name-based inference varies by race/ethnicity and that threshold approaches underestimate Black authors and overestimate White authors. We conclude with recommendations to avoid potential biases. This article lays the foundation for more systematic and less-biased investigations into racial disparities in science. [less ▲]

Intersectional Inequalities in Science

Presentation (2021, December 06)

Avoiding bias when inferring race using name-based approaches

in 18th INTERNATIONAL CONFERENCE ON SCIENTOMETRICS & INFORMETRICS, 12–15 July 2021KU Leuven, Belgium (2021, July)

Racial disparity in academia is a widely acknowledged problem. The quantitative understanding of racial-based systemic inequalities is an important step towards a more equitable research system. However ... [more ▼]

Racial disparity in academia is a widely acknowledged problem. The quantitative understanding of racial-based systemic inequalities is an important step towards a more equitable research system. However, few large-scale analyses have been performed on this topic, mostly because of the lack of robust race-disambiguation algorithms. Identifying author information does not generally include the author’s race. Therefore, an algorithm needs to be employed, using known information about authors, i.e., their names, to infer their perceived race. Nevertheless, as any other algorithm, the process of racial inference can generate biases if it is not carefully considered. When the research is focused on the understanding of racial-based inequalities, such biases undermine the objectives of the investigation and may perpetuate inequities. The goal of this article is to assess the biases introduced by the different approaches used name-based racial inference. We use information from US census and mortgage applications to infer the race of US author names in the Web of Science. We estimate the effects of using given and family names, thresholds or continuous distributions, and imputation. Our results demonstrate that the validity of name-based inference varies by race and ethnicity and that threshold approaches underestimate Black authors and overestimate White authors. We conclude with recommendations to avoid potential biases. This article fills an important research gap that will allow more systematic and unbiased studies on racial disparity in science. [less ▲]

Semantic and Relational Spaces in Science of Science: Deep Learning Models for Article Vectorisation

in Scientometrics (2021)

Over the last century, we observe a steady and exponentially growth of scientific publications globally. The overwhelming amount of available literature makes a holistic analysis of the research within a ... [more ▼]

Over the last century, we observe a steady and exponentially growth of scientific publications globally. The overwhelming amount of available literature makes a holistic analysis of the research within a field and between fields based on manual inspection impossible. Automatic techniques to support the process of literature review are required to find the epistemic and social patterns that are embedded in scientific publications. In computer sciences, new tools have been developed to deal with large volumes of data. In particular, deep learning techniques open the possibility of automated end-to-end models to project observations to a new, low-dimensional space where the most relevant information of each observation is highlighted. Using deep learning to build new representations of scientific publications is a growing but still emerging field of research. The aim of this paper is to discuss the potential and limits of deep learning for gathering insights about scientific research articles. We focus on document-level embeddings based on the semantic and relational aspects of articles, using Natural Language Processing (NLP) and Graph Neural Networks (GNNs). We explore the different outcomes generated by those techniques. Our results show that using NLP we can encode a semantic space of articles, while with GNN we are able to build a relational space where the social practices of a research community are also encoded. [less ▲]

Machine Learning on Graphs

Presentation (2020, November 18)

Graphs are a ubiquitous data structure that can be exploited in many different problems. In tasks where observations are not independently drawn from the data generating process, but their codependencies ... [more ▼]

Graphs are a ubiquitous data structure that can be exploited in many different problems. In tasks where observations are not independently drawn from the data generating process, but their codependencies add valuable information, a network analysis might be useful for modelling those relations. In this seminar we will discuss about Graph Neural Networks, the deep learning approach for dealing with networks. [less ▲]