CoChat
Sign Up
Cited by 3Criminal Justice and Corrections AnalysisCriminal Justice and PenologyCriminal Law and Policy

Modelling Pretrial Detention

Open access
Hamilton, Melissa·2022·SSRN Electronic Journal
DOI 10.2139/ssrn.4188907
Open this paper in CoChat — free account

Abstract

Pretrial detention has become normative in contemporary criminal justice, rather than the exception to a rule of release for individuals not convicted of any crime. Even the opportunity for release with a bond amount is often not in fact enjoyed for the many individuals who are unable to afford to pay. Defendants detained pending trial suffer numerous consequences in the negative impacts to their own legal cases in terms of being more likely to feel pressured to plead guilty and to receive a prison sentence. The high numbers of those detained appear to disproportionately impact minorities and has contributed to mass incarceration. As a result of these issues, the country is in the midst of a third reform movement in terms of policies to increase the rate of pretrial release without financial surety and to incorporate algorithmic risk assessment tools to isolate the few individuals who pose a high likelihood of failure if released pending trial. This Article offers a case study of an important site engaged in pretrial reforms. The research deploys a dataset of defendants booked into jail in Cook County, Illinois (home to Chicago). The study provides an empirical exploration of how the outcome of pretrial detention may be associated with racial and gender disparities and whether any such disparities are ameliorated when considering a host of legal factors that are predictive of pretrial detention. A related research question is how the use of an algorithmic risk tool modifies the relationship between pretrial detention and a combination of demographic factors and judicial decisions about release. Policy implications of the results are informative to debates concerning pretrial reforms in terms of whether risk assessment tools offer the ability to reduce racial/ethnic and gender disparities and to decrease the detention rate. Potential contributions such as this study are timely considering the experiment with decarceration due to COVID concerns which has not been associated with an increased risk to public safety.

Integrity signals

Registry

Crossref · 10.2139/ssrn.4188907

Cites

33 of 33 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

1 of the 34 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Related

Authors
Hamilton, M.
Affiliations
University of Surrey (GB)
Corresponding author
Hamilton, Melissa
ORCID
Hamilton, Melissa
Venue
SSRN Electronic Journal
Publisher
Elsevier BV
Published
2022
ISSN
1556-5068 (ISSN-L)
DOI
10.2139/ssrn.4188907
OpenAlex
W4293071378
Cited by
3
References
34
Citations by year
2026: 1 · 2025: 2
Type
posted content
Language
en
Open access
Locations
  • SSRN Electronic Journal (publishedVersion)
  • bepress Legal Repository (submittedVersion)
Topics
  • Social Sciences › Sociology and Political Science › Criminal Justice and Corrections Analysis
  • Social Sciences › Law › Criminal Justice and Penology
  • Social Sciences › Sociology and Political Science › Criminal Law and Policy
Keywords
pretrial detention, pretrial release, algorithmic risk assessment, racial disparities, gender disparities, mass incarceration, decarceration, COVID-19 pandemic, judicial decision-making, bail, Cook County Jail

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review