Nordasil. Medicinsk bevisning i asylärenden




Principal Investigator:

Rebecca Stern


Uppsala universitet

Start Date:


End Date:


Primary Classification:

50502: Law and Society



  • Castor /proj at UPPMAX: 3000 GiB
  • Cygnus /proj at UPPMAX: 3000 GiB
  • Castor /proj/nobackup at UPPMAX: 2600 GiB
  • Cygnus /proj/nobackup at UPPMAX: 2600 GiB
  • Bianca at UPPMAX: 2 x 1000 core-h/month


The aim of the project is twofold: first, to investigate how medical evidence is understood and applied in the assessment of asylum grounds in order to increase understanding of the asylum process and which factors influence asylum assessments, as well as to analyze aspects of legal certainty; secondly, to develop new interdisciplinary (law, computer science, medicine, linguistics) methods that enable analyzes on an aggregate level based on which generalizable conclusions about asylum assessments can be drawn. The project data consists of copies of the Migration Agency's asylum decisions from the years 2006-2020 (category A1 asylum). It is only the legal assessments that are subject to analysis. No annexes are attached to the decisions or included in the study. The research methods in the project involve both qualitative and quantitative analysis. In the qualitative part, a strategic selection of 1-200 decisions is initially read manually partly for the purpose of creating codes for different types/categories of information, partly to make a qualitative assessment of whether/how medical evidence is used in the asylum assessment. In the quantitative analysis, in which the overwhelming majority of decisions are analyzed, the files are converted into machine-readable text files through optical character recognition (OCR). The text content of the files is then filtered using different types of natural language processing (NPL). Through these data analyses, combinations of information categories can be examined and analyzed at an aggregated level. In a third step, the results are analyzed using static analysis models and machine learning (AI) technology. The results of the data analysis are examined and explained in text and through matrices and map images. The quantitative study is followed up by a manual analysis of an additional 2-300 decisions to validate the findings and refine the algorithms.