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When do governments at one level of a federal system choose to enter a policy domain traditionally or legally reserved for a different level? While entering a new policy domain expands the power of the leaders, it is a risky gambit. Such a move can provoke conflicts over jurisdictions with authorities at higher levels of government, open local leaders to blame if their policies are unsuccessful, and present substantial strains on limited municipal budgets. This paper investigates such processes of unilateral municipalization from below through the case of municipal public security policies in Brazil, a policy area reserved almost entirely for state governments in the 1988 constitution. A semi-automated textual analysis of over 60,000 Brazilian mayoral electoral platforms across three electoral cycles measures the occurrence of major policy areas including healthcare, education, and public security in mayoral platforms. The supervised machine learning method utilized in the analysis presents a transparent, reproduceable, and easily validated way to study large corpora of political texts. The performance of various classification algorithms are compared with the best performing algorithm then used to measure the relative emphasis given to public security in individual platforms, finding that political factors including the presence of a candidate with a professional background in public security, partisan linkages to the federal government, and influence from neighboring municipalities influences engagement with public security more than underlying environmental factors such as crime rates.