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Project Recon : = A Computational Framework for and Analysis of the California Parole Hearing System.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Project Recon :/
Reminder of title:	A Computational Framework for and Analysis of the California Parole Hearing System.
Author:	Hong, Jenny.
Description:	1 online resource (197 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-12, Section: A.
Contained By:	Dissertations Abstracts International84-12A.
Subject:	Language. -
Online resource:	click for full text (PQDT)
ISBN:	9798379653576

Project Recon : = A Computational Framework for and Analysis of the California Parole Hearing System.
Hong, Jenny.

Project Recon :A Computational Framework for and Analysis of the California Parole Hearing System. - 1 online resource (197 pages)

Source: Dissertations Abstracts International, Volume: 84-12, Section: A.

Thesis (Ph.D.)--Stanford University, 2023.

Includes bibliographical references

Parole decisions can tip a sentence toward fifteen years or fifty. Despite the great power that parole boards hold, their decision processes are poorly documented and largely hidden from public scrutiny. Parole hearings produce almost no structured data, only an unstructured transcript of hearing dialogue several hundred pages in length. In the following dissertation, we use natural language processing to analyze the transcripts of 35,105 parole hearings held between 2007 and 2019 for candidates serving life sentences in California, totalling approximately five million pages. Through regression analyses of data extracted from the transcripts, after controlling for relevant case factors, we find that several factors outside of the candidate's control explain hearing outcomes. We find that commissioners vary widely in their punitiveness in previously unobserved ways; the assignment to a particular commissioner significantly influences the hearing outcome. Racial disparities limit the quality of legal representation that parole candidates receive as well as their voice in the hearing dialogue, and both significantly predict the parole outcome after again controlling for case factors. Previous analyses of parole systems have been limited by the unavailability of structured data or the task of hand-annotating hearing transcripts. Our results thus provide the most comprehensive picture of a parole system studied to date. While our results carry direct implications for legislative parole reform, our methodology-using machine learning to analyze legal hearings-can be extended to many other procedures in criminal and administrative law with limited structured data.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379653576Subjects--Topical Terms:

571568
Language.
Index Terms--Genre/Form:

554714
Electronic books.

Project Recon : = A Computational Framework for and Analysis of the California Parole Hearing System.
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500 $a Source: Dissertations Abstracts International, Volume: 84-12, Section: A.
500 $a Advisor: Ugander, Johan;Manning, Christopher.
502 $a Thesis (Ph.D.)--Stanford University, 2023.
504 $a Includes bibliographical references
520 $a Parole decisions can tip a sentence toward fifteen years or fifty. Despite the great power that parole boards hold, their decision processes are poorly documented and largely hidden from public scrutiny. Parole hearings produce almost no structured data, only an unstructured transcript of hearing dialogue several hundred pages in length. In the following dissertation, we use natural language processing to analyze the transcripts of 35,105 parole hearings held between 2007 and 2019 for candidates serving life sentences in California, totalling approximately five million pages. Through regression analyses of data extracted from the transcripts, after controlling for relevant case factors, we find that several factors outside of the candidate's control explain hearing outcomes. We find that commissioners vary widely in their punitiveness in previously unobserved ways; the assignment to a particular commissioner significantly influences the hearing outcome. Racial disparities limit the quality of legal representation that parole candidates receive as well as their voice in the hearing dialogue, and both significantly predict the parole outcome after again controlling for case factors. Previous analyses of parole systems have been limited by the unavailability of structured data or the task of hand-annotating hearing transcripts. Our results thus provide the most comprehensive picture of a parole system studied to date. While our results carry direct implications for legislative parole reform, our methodology-using machine learning to analyze legal hearings-can be extended to many other procedures in criminal and administrative law with limited structured data.
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538 $a Mode of access: World Wide Web
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