@inproceedings{dornelles-2026-democratizing,
    title = "Democratizing Legal Analytics: Resource-Efficient Information Extraction for {B}razilian Case Law",
    author = "Dornelles, Rodrigo Filippi",
    editor = "Souza, Marlo  and
      de-Dios-Flores, Iria  and
      Santos, Diana  and
      Freitas, Larissa  and
      Souza, Jackson Wilke da Cruz  and
      Ribeiro, Eug{\'e}nio",
    booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 1",
    month = apr,
    year = "2026",
    address = "Salvador, Brazil",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-dnd/2026.propor-1.103/",
    pages = "1011--1020",
    ISBN = "979-8-89176-387-6",
    abstract = "Legal systems produce large volumes of high-stakes decisions in unstructured natural language, making large-scale empirical analysis costly, difficult to reproduce, and unevenly accessible. This bottleneck is especially acute for legal analytics and policy evaluation in low-resource languages such as Portuguese. To address it, we present a resource-efficient pipeline for information extraction from Brazilian criminal case law that reuses a legacy dataset to fine-tune open-weight LLMs with Q-LoRA. Operating in a small-data setting and using schema-constrained JSON generation, the pipeline extracts 47 legal variables spanning charges, evidence, and sentencing outcome. In held-out evaluation, a fine-tuned Phi-4 (14B) model achieves 92.8{\%} accuracy and 0.826 macro-F1, approaching proprietary baselines while retaining the cost and privacy benefits of local deployment. We then use the extracted data in a case study of the short-term effects of a recent Brazilian Supreme Court ruling on drug decriminalization, finding no statistically significant change in trafficking-conviction rates (p{\ensuremath{\geq}}0.05), a pattern consistent with short-run institutional inertia. More broadly, the paper contributes a reproducible framework for legal NLP and shows how legacy empirical datasets can support scalable legal analytics under severe resource constraints."
}