Sounds like another 10 to me!

@maastrichtu.bsky.social

The issue? Selection bias. Winners are a nonrepresentative subset of the applicant pool, and failing to account for this can distort our understanding of inequality. In the paper, we use causal inference tools to illustrate this bias and offer strategies to address it.

While it might seem intuitive to compare the proportion of women vs. men among recipients, this approach can lead to misleading conclusions (e.g., underestimating disparities).

So, what is our paper about? The core idea is simple: When quantifying group-based disparities—such as the gender gap in receiving prestigious grants—we can’t just examine the winners.

A huge thank you as well to our reviewers and editor, Dave Sbarra @dsbarra.bsky.social , for seeing the potential in our work and helping us refine it into something stronger and more accessible.

Before diving into the paper, I want to express my deep gratitude to Cathy Johnson and Karen Hegtvedt—two incredible sociologists whose work inspired us to pursue this project.

My coauthor, Wen Wei Loh, and I had the pleasure to talk with Özge Fischer-Baum about our work in a new episode of #UndertheCortex, the podcast of APS @psychscience.bsky.social

Welcome! 🤗

Thanks for doing this! Could you add me please?

Amazing work. Love it!