Core Anomalous Experiences Shared Across Diverse Citizen Science Datasets

Abstract

Scientific investigation of unidentified anomalous phenomena (UAP) extends to the early origins of citizen science. Scientific activity by non-professionals (citizen science) has historically produced real discoveries, often focuses on subjects understudied by professional scientists, involves interaction with speculative fiction, and has a century-long history of amassing large databases of anomalous events. Through historical analysis, I show that while it exists in a liminal space adjacent to professional science, citizen science of anomalies is a reasonably coherent scholarly tradition. I then engage this tradition by applying modern machine learning and artificial intelligence tools to four anomalous events databases to examine whether quantitative lexical analysis can extract a core anomalous experience that is shared in common across them. The analysis identifies a cluster of documents from diverse authorship sources. This document cluster appears to have greater frequencies of many themes that were proposed by prior qualitative analysis to be part of the core anomalous experience.