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Overview of “The past as prologue? Decarceration in California then and now”

2011· article· en· W1902480572 on OpenAlexaff
Rosemary Gartner, Anthony N. Doob, Franklin E. Zimring

Bibliographic record

VenueCriminology & Public Policy · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsImprisonmentPrisonPoliticsPolitical scienceState (computer science)GovernorPopulationCriminologyLawSociologyDemography

Abstract

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Research Summary In 1968, California Governor Ronald Reagan's second year in office, the imprisonment rate in the state's institutions was 146 per 100,000 residents. In 1972, California's state prisons incarcerated 96 prisoners per 100,000 residents – a decrease of 34% and the state's lowest level of imprisonment since at least 1950. This study examines how this reduction was accomplished during the tenure of a governor elected in part because of his tough approach to crime and disorder. We find that the decrease in the prison population resulted from a confluence of events, rather than a single dominant cause, and that this process extended over a period of years, rather than being limited to Reagan's first year or two in office. There are lessons of value in this history for California's and other states’ current problems with high imprisonment rates. However, differences in law, in the scale of imprisonment, and in the politics of penality are likely to limit efforts to substantially educe prison populations in California and elsewhere. Policy Implications The growth of the level of imprisonment in the United States, coupled with financial and legal pressures to reduce those levels to something more sensible and financially viable, provides a strong impetus for understanding how imprisonment rates can be controlled. For jurisdictions facing court or financial pressures to reduce imprisonment, successful efforts to lower prison populations, whether in the United States or elsewhere, should be of particular relevance. However, although much has been written about the reasons for the enormous growth in imprisonment in the United States since the mid-1970s, the study of effective ways to limit this growth or reverse it has been described as ‘virgin territory.’ It is reasonably well established that the size of a jurisdiction's prison population is a function of imprisonment policies rather than crime or arrest rates. Thus, to understand trends in imprisonment rates one needs to examine criminal justice policies that determine who and how many go into prison, how long they stay there, and whether, after release, they are returned to prison if they violate release conditions. It is not, however, the case that reducing imprisonment can be accomplished by simply adjusting one of these levers or reversing one of the policies that led to an increase in imprisonment. California's brief but dramatic experience with decarceration in the late 1960s and early 1970s illustrates these points. The 34% reduction in the state's prison population between 1967 and 1971 – which Governor Reagan celebrated in his second inaugural address – resulted from a number of quite different changes occurring over more or less the same span of time. One factor that was not responsible, however, was crime: Reported crime rates and felony arrest rates were increasing at the same time imprisonment was decreasing. Instead, the reduction was due to (a) a decrease in the probability of a prison sentence (due to a program that subsidized counties for placing offenders on probation rather than sending them to state prison), (b) an increase in the rate of release from prison (due to a decrease in length of time served before parole release), and (c) a decrease in the rate of return to imprisonment as a consequence of parole failure (due to a change in practice by the Adult Authority, California's parole board). Recently, a few states – including New York, Michigan, Oregon, and Colorado – have experimented with policy changes to halt or reverse the growth in their prison populations. Among the reforms tried are elimination of some mandatory minimum sentences, revisions to sentencing laws that return discretion to judges, amendments to truth-in-sentencing laws, and changes in drug policies and laws. Indeed, early in 2010 the California Department of Corrections and Rehabilitation launched its own reforms to shrink its inmate numbers in response to both a federal court order and a budgetary crisis; among these was the creation of “nonrevocable parole” for low-risk parolees. Unfortunately, most of these efforts have had at best modest effects on prison growth and many have been or are being challenged by public interest groups and politicians. Does the Reagan-era reduction in imprisonment offer any insights into achieving nontrivial reductions in prison populations? In the 1960s and early 1970s, California produced a relatively low-visibility, multiyear program at the state level of government. Major legislative changes were not required, however, multiple changes were. Substantial levels of decarceration cannot be achieved by reducing commitments to prison or increasing parole release or decreasing prison return after parole failure. All three components must happen together to have important effects. That is the first lesson from the Reagan-era episode. The second is that the reduction in imprisonment was the result of a process that extended over a period of years rather than instantly, due to a single policy or procedural change. However, there are now impediments to reform in California (and elsewhere) that did not exist 40 years ago, including a series of changes in (a) the scale of imprisonment, (b) state finances, (c) state-level power to set prison terms, (d) the visibility of penal policy, (e) the relative influence of state administrators and the public on correctional policy, and (f) beliefs about the efficacy of imprisonment. These limit the capacity of a twenty-first century governor to act and alter the publicity and controversy that will accompany any major reduction in imprisonment. Large-scale decarceration is more difficult in 2010 than in the 1960s, although not impossible. Big changes will require visible policy shifts and legislative as well as executive branch participation. The decisions that are the targets for substantial decarceration have not changed since the Reagan years, but the mechanics of achieving shifts involve much more cooperation within and across levels of government.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.164
GPT teacher head0.356
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2011
Admission routes1
Has abstractyes

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