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The current study examines how key internal U.S. Department of Justice (DOJ) policy changes have been translated into front-line prosecutorial practices. Extending courts-as-communities scholarship and research on policy implementation practices, we use U.S. Sentencing Commission data from 2004 to 2019 to model outcomes for several measures of prose- cutorial discretion in federal drug trafficking cases, including the use of mandatory minimum charges and prosecutor-endorsed departures, to test the impact of the policy changes on case processing outcomes. We contrast prosecutorial measures with measures that are more impervious to discretionary manipulation, such as criminal history, and those that represent judicial and blended discretion, including judicial departures and final sentence lengths. We find a significant effect of the policy reforms on how prosecutorial tools are used across DOJ policy periods, and we find variation across districts as a function of contextual conditions, consistent with the court communities literature. We also find that a powerful driver of changes in pros- ecutorial practices during our most recent period is the confirmation of individual Trump-appointed U.S. Attorneys at the district level, suggesting an important theoretical place for midlevel actors in policy translation and implementation.
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PRESIDE: A Judge Entity Recognition and Disambiguation Model for US District Court Records
The docket sheet of a court case contains a wealth of information about the progression of a case, the parties’ and judge’s decision-making along the way, and the case’s ultimate outcome that can be used in analytical applications. However, the unstructured text of the docket sheet and the terse and variable phrasing of docket entries require the development of new models to identify key entities to enable analysis at a systematic level. We developed a judge entity recognition language model and disambiguation pipeline for US District Court records. Our model can robustly identify mentions of judicial entities in free text (~99% F-1 Score) and outperforms general state-of-the-art language models by 13%. Our disambiguation pipeline is able to robustly identify both appointed and non-appointed judicial actors and correctly infer the type of appointment (~99% precision). Lastly, we show with a case study on in forma pauperis decision-making that there is substantial error (~30%) attributing decision outcomes to judicial actors if the free text of the docket is not used to make the identification and attribution.
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- Award ID(s):
- 2033604
- PAR ID:
- 10352507
- Date Published:
- Journal Name:
- 2021 IEEE International Conference on Big Data (Big Data)
- Page Range / eLocation ID:
- 2721 to 2728
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BigData52589.2021.9671351
