ANR-Lab Scientific Seminar 'Opportunities and limitations of the analysis of open court data: case of administrative cases data on protests on the Moscow City Court Website'

The paper examines the influence of the gender of the judge and the gender of the accused on the decision on guilt, the type and degree of punishment in various parts of Article 20.2 of the Code of Administrative Offenses of the Russian Federation – for participating in and organizing protest actions. In addition, socio-demographic characteristics are also considered as control variables. The research question is what kind of influence gender of judges and gender of the accused will have on the type and degree of punishment: for example, are female judges judged more severely than male judges, and are female accused judged more leniently? As data, we used court decisions collected by us independently and processed using NLP methods from the website of the Moscow City Court, turned into tabular data on sentences. As a data analysis method, regression data analysis was used, namely, three regression models: a binary logistic model for the type of punishment, a linear model for the degree of arrest, and a multinomial model for the degree of fine.

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We will be glad to see everyone!