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In this study, we extract detailed information on traffic-related criminal behavior from Slovak court verdicts and link it with comprehensive administrative data. We first discuss the challenges in identifying traffic offenses, categorizing them into analytically meaningful subgroups, and determining which variables we can reliably extract from the data. We then analyze the dataset using two complementary approaches. First, we present regression models incorporating detailed information about the offense and the offender. Second, we introduce a novel method for analyzing sentencing practices by identifying the most common crimes and their sentences. This approach aligns with the narrative-based analysis of sentencing that other jurisdictions have partially applied, such as the study of normal cases and punishments in Finland.
Tomáš Knap, Charles University; Institute of State and Law of the Czech Academy of Sciences
Jakub Drápal, Charles University; Institute of State and Law of Czech Academy of Sciences
Klára Bendová, Faculty of Law, Charles University
Jan Černý, Faculty of Law, Charles University
Vojtěch Pour, Faculty of Law, Charles University