SODAS Data Discussion 1 (Spring 2025)
Copenhagen Center for Social Data Science (SODAS) aspirers to be a resource for all students and researchers at the Faculty of Social Sciences. We therefore invite researchers across the faculty to present ongoing research projects, project applications or just a loose idea that relates to the subject of social data science.
Two researchers will present their work. The rules are simple: Short research presentations of ten minutes are followed by twenty minutes of debate. No papers will be circulated beforehand, and the presentations cannot be longer than five slides.
Discussion 1
Presenter: Paw Randrup
Title: Exploring the Phenomenology of Psychedelic Experiences with AI
Abstract:
Intro: Generative Artificial Intelligence (AI), particularly Large Language Models (LLMs), may have great potential in analysing qualitative research data. This study examines the feasibility of an LLM performing Reflexive Thematic Analysis (RTA) and its ability to replicate findings from a dataset previously analyzed by human researchers.
Methods: The original dataset consisted of transcribed, semi-structured interviews from 20 participants who completed a Guided Music and Visualization Exercise (GMVA) before a clinical psilocybin session. The study aimed to explore the GMVA’s effectiveness as a preparatory tool, and applied Braun and Clarke’s (2006) six-phase RTA to identify explicit and latent patterns of meaning in the data. This study evaluates whether a LLM can replicate the findings of the original analysis by applying the same six-phase RTA framework to synthetic data designed to mirror the original dataset. The LLM’s thematic outputs are compared to those of human analysts, with particular attention to how each phase of RTA is operationalized from an LLM perspective. The proprietary model used was ChatGPT-4 Mini (OpenAI).
Results: Findings suggest that LLMs can infer key themes, demonstrating the potential viability of AI-assisted RTA. However, LLMs’ reliance on probabilistic structures rather than human intuition and contextual depth presents notable limitations.
Conclusion: By exploring this emerging methodology, this study provides preliminary insights into the integration of LLMs into qualitative research workflows and reflects on their potential to complement, rather than replace, human analysis.
Discussion 2
Presenter: Yevgeniy Golovchenko
Title: Sanction Evasion and Covert Maritime Networks
Abstract:
In the past decade, a global “shadow fleet” of vessels has emerged to evade sanctions, violating international maritime laws and supporting smuggling activities that fund wars, such as Russia’s invasion of Ukraine. These fleets, often non-compliant with safety regulations and pose significant environmental risks. Despite these threats, shadow shipping remains understudied. This project conducts a systematic analysis of shadow fleet operations, using geospatial vessel tracking data to build a global oil tanker dataset. Focusing on Russia, which has constructed the largest shadow fleet to bypass EU and G7 sanctions, the study employs social data science methods to explore maritime sanctions evasion strategies. By bridging social data science methods and maritime security studies, the research fills a critical knowledge gap and demonstrates how ship tracking data can enhance social science methodologies for analyzing illicit maritime networks.