From Punishment to Protection: Charting Six Decades of U.S. Juvenile Justice Through Topic Modeling and LLM-Assisted Analysis

Abstract

Juvenile courts handle two very different kinds of cases: young people accused of crimes, and children at risk in their own families, and both streams have been changing dramatically over the past fifty years. This paper asks: what has shifted, and can computational methods track that change at scale? Topic modeling and LLM-assisted trend analysis is applied to 60,470 U.S. appellate opinions spanning 1970 to 2025, identifying 182 distinct legal topics organized into 10 themes covering the full range of juvenile justice litigation. The results are striking. Child welfare litigation tripled its share of the corpus. Sex offender registration cases more than doubled. Traditional punitive mechanisms, judicial transfer to adult court and the juvenile death penalty, declined sharply. A new cluster of sentencing cases emerged after 2010, reflecting landmark Supreme Court rulings that fundamentally redrew the constitutional limits on juvenile punishment. Analysis also shows that legal vocabulary shifts decade by decade: the language courts used in the 1970s can be unrecognisable by the 2020s, even for the same underlying legal question. The fastest-growing area of the corpus has fractured into dozens of jurisdiction-specific variants that no single topic can capture. In both cases, case counts alone would miss the full arc of doctrinal change. This paper demonstrates that large-scale, reproducible analysis of appellate case law, quantitative trends and doctrinal arcs alike, is possible and practically useful. It also reveals critical risks that any AI-based decision support tool used in juvenile justice and trained on such corpus will encounter: temporal mismatch, vocabulary drift, jurisdictional fragmentation, and the divergence of delinquency and child welfare into two parallel legal systems. Addressing these risks must be a fundamental requirement for any tool used in this domain.