Reposted by Albert Varela
New blog post: "Cluster analysis: A skeptic’s guide".
In which I ask a few questions that social scientists wishing to identify hidden classes in their data ought to address.
janhove.github.io/posts/2026-0...
Lecturer in Quantitative Methods - School of Sociology and Social Policy, University of Leeds. Interested in measurement and analysis of job quality, poverty and social mobility.
!["Bann et al. pose the question, ‘why can’t epidemiology be automated (yet)?’, as though the automation of epidemiology is both inevitable and desirable [1]. They describe a future of ‘1-click reviews’ and ‘end-to-end automation’ that will ‘[boost] the efficiency of current practice and … [create] new opportunities for’. But scientific discovery requires far more than increased efficiency. In epidemiology, new knowledge comes from critical reasoning about data, about context, and about the limits of our existing understanding. Such reasoning, in turn, demands deep thought, scholarship, and diligence. All are threatened by automation..."](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:7pr24ntsdnvee23td552aes4/bafkreihsubff7cl2mwukra4tmn3tqqkzrmuzik57g6jmtmt6jftjghfska)
