The National Prisoner Statistics (NPS) data collection began in 1926 in response to a congressional mandate to gather information on persons incarcerated in state and federal prisons. Originally under the auspices of the U.S. Census Bureau, the collection moved to the Bureau of Prisons in 1950, and then in 1971 to the National Criminal Justice Information and Statistics Service, the precursor to the Bureau of Justice Statistics (BJS) which was established in 1979. From 1979 to 2013, the Census Bureau was the NPS data collection agent. In 2014, the collection was competitively bid in conjunction with the National Corrections Reporting Program (NCRP), since many of the respondents for NPS and NCRP are the same. The contract was awarded to Abt Associates, Inc. The NPS is administered to 51 respondents. Before 2001, the District of Columbia was also a respondent, but responsibility for housing the District of Columbia's sentenced prisoners was transferred to the Federal Bureau of Prisons, and by yearend 2001 the District of Columbia no longer operated a prison system. The NPS provides an enumeration of persons in state and federal prisons and collects data on key characteristics of the nation's prison population. NPS has been adapted over time to keep pace with the changing information needs of the public, researchers, and federal, state, and local governments.

This data collection includes tabulations of annual adult admissions to federal and state correctional institutions by gender for the years 1926 through 1987. The two data files have identical structures: Part 1 includes information on male admissions, and Part 2 includes information on female admissions. The 3,348 cases in each part include one case for each of the 62 years of the collection for each of the following 54 categories: the 50 states, the District of Columbia, federal institutional totals, state cumulative totals, and United States totals (the sum of the federal and state cumulative totals). The figures were drawn from a voluntary reporting program in which each state, the District of Columbia, and the Federal Bureau of Prisons reported summary and detailed statistics, as part of the National Prisoner Statistics reporting series. Each file also includes individual state and United States general population figures.

Prison Inmates in India

Demographics, Age, Education, Caste, Wages, Rehabilitation, Technical Info

By Rajanand Ilangovan [source]

About this dataset

This dataset provides a detailed view of prison inmates in India, including their age, caste, and educational background. It includes information on inmates from all states/union territories for the year 2019 such as the number of male and female inmates aged 16-18 years, 18-30 year old inmates and those above 50 years old. The data also covers total number of penalized prisoners sentenced to death sentence, life imprisonment or executed by the state authorities. Additionally, it provides information regarding the crimehead (type) committed by an inmate along with its grand total across different age groups. This dataset not only sheds light on India’s criminal justice system but also highlights prevelance of crimes in different states and union territories as well as providing insight into crime trends across Indian states over time

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How to use the dataset

This dataset provides a comprehensive look at the demographics, crimes and sentences of Indian prison inmates in 2019. The data is broken down by state/union territory, year, crime head, age groups and gender.

This dataset can be used to understand the demographic composition of the prison population in India as well as the types of crimes committed. It can also be used to gain insight into any changes or trends related to sentencing patterns in India over time. Furthermore, this data can provide valuable insight into potential correlations between different demographic factors (such as gender and caste) and specific types of crimes or length of sentences handed out.

To use this dataset effectively there are a few important things to keep in mind: •State/UT - This column refers to individual states or union territories in India where prisons are located •Year – This column indicates which year(s) the data relates to •Both genders - Female columns refer only to female prisoners while male columns refers only to male prisoners •Age Groups – 16-18 years old = 21-30 years old = 31-50 years old = 50+ years old •Crime Head – A broad definition for each type of crime that inmates have been convicted for •No Capital Punishment – The total number sentenced with capital punishment No Life Imprisonment – The total number sentenced with life imprisonment No Executed– The total number executed from death sentence Grand Total–The overall totals for each category

By using this information it is possible to answer questions regarding topics such as sentencing trends, types of crimes committed by different age groups or genders and state-by-state variation amongst other potential queries

Research Ideas

• Using the age and gender information to develop targeted outreach strategies for prisons in order to reduce recidivism rates.
• Creating an AI-based predictive model to predict crime trends by analyzing crime head data from a particular region/state and correlating it with population demographics, economic activity, etc.
• Analyzing the caste of inmates across different states in India in order to understand patterns of discrimination within the criminal justice system

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original.

Columns

File: SLL_Crime_headwise_distribution_of_inmates_who_convicted.csv | Column name | Description | |:--------------------------|:---------------------------------------------------------------------------------------------------| | STATE/UT | Name of the state or union territory where the jail is located. (String) | | YEAR | Year when the inmate population data was collected. (Integer) ...

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study is a secondary analysis of data from ICPSR Study Number 27101, Serious and Violent Offender Reentry Initiative (SVORI) Multi-site Impact Evaluation, 2004-2011 [United States]- specifically the adult male dataset -to examine the associations among child support obligations, employment and reentry outcomes. The study addressed the following research questions: Are the demographic, criminal justice and employment-related characteristics of incarcerated men with child support orders significantly different in any important way from incarcerated males without child support orders? Did SVORI clients receive more support and services related to child support orders and modification of debt after release from prison compared to non-SVORI participants? Does having legal child support obligations decrease the likelihood of employment in later waves, net of key demographic and criminal justice history factors? How does employment influence the relationship between child support debt and recidivism? and Is family instrumental support a significant predictor of reduced recidivism or increased employment in models assessing the relationship between child support obligations, employment and recidivism? The study includes one document (Syntax_ChildSupport_Reentry_forICPSR_2012-IJ-CX-0012.docx) which contains SPSS and Stata syntax used to create research variables.

Overview

The Marshall Project, the nonprofit investigative newsroom dedicated to the U.S. criminal justice system, has partnered with The Associated Press to compile data on the prevalence of COVID-19 infection in prisons across the country. The Associated Press is sharing this data as the most comprehensive current national source of COVID-19 outbreaks in state and federal prisons.

Lawyers, criminal justice reform advocates and families of the incarcerated have worried about what was happening in prisons across the nation as coronavirus began to take hold in the communities outside. Data collected by The Marshall Project and AP shows that hundreds of thousands of prisoners, workers, correctional officers and staff have caught the illness as prisons became the center of some of the country’s largest outbreaks. And thousands of people — most of them incarcerated — have died.

In December, as COVID-19 cases spiked across the U.S., the news organizations also shared cumulative rates of infection among prison populations, to better gauge the total effects of the pandemic on prison populations. The analysis found that by mid-December, one in five state and federal prisoners in the United States had tested positive for the coronavirus -- a rate more than four times higher than the general population.

This data, which is updated weekly, is an effort to track how those people have been affected and where the crisis has hit the hardest.

Methodology and Caveats

The data tracks the number of COVID-19 tests administered to people incarcerated in all state and federal prisons, as well as the staff in those facilities. It is collected on a weekly basis by Marshall Project and AP reporters who contact each prison agency directly and verify published figures with officials.

Each week, the reporters ask every prison agency for the total number of coronavirus tests administered to its staff members and prisoners, the cumulative number who tested positive among staff and prisoners, and the numbers of deaths for each group.

The time series data is aggregated to the system level; there is one record for each prison agency on each date of collection. Not all departments could provide data for the exact date requested, and the data indicates the date for the figures.

To estimate the rate of infection among prisoners, we collected population data for each prison system before the pandemic, roughly in mid-March, in April, June, July, August, September and October. Beginning the week of July 28, we updated all prisoner population numbers, reflecting the number of incarcerated adults in state or federal prisons. Prior to that, population figures may have included additional populations, such as prisoners housed in other facilities, which were not captured in our COVID-19 data. In states with unified prison and jail systems, we include both detainees awaiting trial and sentenced prisoners.

To estimate the rate of infection among prison employees, we collected staffing numbers for each system. Where current data was not publicly available, we acquired other numbers through our reporting, including calling agencies or from state budget documents. In six states, we were unable to find recent staffing figures: Alaska, Hawaii, Kentucky, Maryland, Montana, Utah.

To calculate the cumulative COVID-19 impact on prisoner and prison worker populations, we aggregated prisoner and staff COVID case and death data up through Dec. 15. Because population snapshots do not account for movement in and out of prisons since March, and because many systems have significantly slowed the number of new people being sent to prison, it’s difficult to estimate the total number of people who have been held in a state system since March. To be conservative, we calculated our rates of infection using the largest prisoner population snapshots we had during this time period.

As with all COVID-19 data, our understanding of the spread and impact of the virus is limited by the availability of testing. Epidemiology and public health experts say that aside from a few states that have recently begun aggressively testing in prisons, it is likely that there are more cases of COVID-19 circulating undetected in facilities. Sixteen prison systems, including the Federal Bureau of Prisons, would not release information about how many prisoners they are testing.

Corrections departments in Indiana, Kansas, Montana, North Dakota and Wisconsin report coronavirus testing and case data for juvenile facilities; West Virginia reports figures for juvenile facilities and jails. For consistency of comparison with other state prison systems, we removed those facilities from our data that had been included prior to July 28. For these states we have also removed staff data. Similarly, Pennsylvania’s coronavirus data includes testing and cases for those who have been released on parole. We removed these tests and cases for prisoners from the data prior to July 28. The staff cases remain.

About the Data

There are four tables in this data:

• covid_prison_cases.csv contains weekly time series data on tests, infections and deaths in prisons. The first dates in the table are on March 26. Any questions that a prison agency could not or would not answer are left blank.

• prison_populations.csv contains snapshots of the population of people incarcerated in each of these prison systems for whom data on COVID testing and cases are available. This varies by state and may not always be the entire number of people incarcerated in each system. In some states, it may include other populations, such as those on parole or held in state-run jails. This data is primarily for use in calculating rates of testing and infection, and we would not recommend using these numbers to compare the change in how many people are being held in each prison system.

• staff_populations.csv contains a one-time, recent snapshot of the headcount of workers for each prison agency, collected as close to April 15 as possible.

• covid_prison_rates.csv contains the rates of cases and deaths for prisoners. There is one row for every state and federal prison system and an additional row with the National totals.

Queries

The Associated Press and The Marshall Project have created several queries to help you use this data:

Get your state's prison COVID data: Provides each week's data from just your state and calculates a cases-per-100000-prisoners rate, a deaths-per-100000-prisoners rate, a cases-per-100000-workers rate and a deaths-per-100000-workers rate here

Rank all systems' most recent data by cases per 100,000 prisoners here

Find what percentage of your state's total cases and deaths -- as reported by Johns Hopkins University -- occurred within the prison system here

Attribution

In stories, attribute this data to: “According to an analysis of state prison cases by The Marshall Project, a nonprofit investigative newsroom dedicated to the U.S. criminal justice system, and The Associated Press.”

Contributors

Many reporters and editors at The Marshall Project and The Associated Press contributed to this data, including: Katie Park, Tom Meagher, Weihua Li, Gabe Isman, Cary Aspinwall, Keri Blakinger, Jake Bleiberg, Andrew R. Calderón, Maurice Chammah, Andrew DeMillo, Eli Hager, Jamiles Lartey, Claudia Lauer, Nicole Lewis, Humera Lodhi, Colleen Long, Joseph Neff, Michelle Pitcher, Alysia Santo, Beth Schwartzapfel, Damini Sharma, Colleen Slevin, Christie Thompson, Abbie VanSickle, Adria Watson, Andrew Welsh-Huggins.

Questions

If you have questions about the data, please email The Marshall Project at info+covidtracker@themarshallproject.org or file a Github issue.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

In order to develop a better understanding of the factors that influence whether a male prisoner's family stays involved in his life during incarceration, researchers conducted face-to-face interviews with inmates from two New Jersey prisons and their family members between May 2005 and July 2006. A total of 35 (25 from one prison and 10 from the other) inmates and 15 family members were interviewed, comprising 13 inmate and family dyads, 1 inmate and family triad, and an additional 21 inmate interviews. The data include variables that explore the family's relationship with the incarcerated individual in the following areas: the inmate's relationship with the family prior to the incarceration, the strain (emotional, economic, stigma) that the incarceration has placed on the family, the economic resources available to the family to maintain the inmate, the family's social support system, and the inmate's efforts to improve or rehabilitate himself while incarcerated.

Summary: This is a collection of publicly reported data relevant to the COVID-19 pandemic scraped from state and federal prisons in the United States. Data are collected each night from every state and federal correctional agency’s site that has data available. Data from Massachusetts come directly from the ACLU Massachusetts COVID-19 website (https://data.aclum.org/sjc-12926-tracker/), not the Massachusetts DOC website. Data from a small number of states come from Recidiviz (https://www.recidiviz.org/) whose team manually collects data from these states. Not all dates are available for some states due to websites being down or changes to the website that cause some data to be missed by the scraper.The data primarily cover the number of people incarcerated in these facilities who have tested positive, negative, recovered, and have died from COVID-19. Many - but not all - states also provide this information for staff members. This dataset includes every variable that any state makes available. While there are dozens of variables in the data, most apply to only a small number of states or a single state.The data is primarily at the facility-date unit, meaning that each row represents a single prison facility on a single date. The date is the date we scraped the data (we do so each night between 9pm-3am EST) and not necessarily the date the data was updated. While many states update daily, some do so less frequently. As such, you may see some dates for certain states contain the same values. A small number of states do not provide facility-level data, or do so for only a subset of all the variables they make available. In these cases we have also collected state-level data and made that available separately. Please note: When facility data is available, the state-level file combines the aggregated facility-level data with any state-level data that is available. You should therefore use this file when doing a state-level analysis instead of aggregating the facility-level data, as some states report values only at the state level (these states may still have some data at the facility-level), and some states report cumulative numbers at the state level but do not report them at the facility level. As a result, when we identify this, we typically add the cumulative information to the state level file. The state level file is still undergoing quality checks and will be released soon.These data were scraped from nearly all state and federal prison websites that make their data available each night for several months, and we continue to collect data. Over time some states have changed what variables are available, both adding and removing some variables, as well as the definition of variables. For all states and time periods you are using this data for, please carefully examine the data to detect these kinds of issues. We have spent extensive time doing a careful check of the data to remove any issues we find, primarily ones that could be caused by a scraper not working properly. However, please check all data for issues before using it. Contact us at covidprisondata@gmail.com to let us know if you find any issues, have questions, or if you would like to collaborate on research.

Content

Represents inmates under custody in NYS Department of Corrections and Community Supervision as of March 31 of the snapshot year. Includes data about admission type, county, gender, age, race/ethnicity, crime, and facility.

Context

This is a dataset hosted by the State of New York. The state has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York State using Kaggle and all of the data sources available through the State of New York organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Mitch Lensink on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

The U.S. government does not release jail by jail mortality data, keeping the public and policy makers in the dark about facilities with high rates of death. In a first-of-its-kind accounting, Reuters obtained and is releasing that data to the public.

What if the jail in your community had an outsized death rate, but no one knew? For decades, communities across the country have faced that quandary. The Justice Department collects jail death data, but locks the information away, leaving policymakers, investigators and activists unaware of problem facilities.

Reuters journalists filed more than 1,500 public records requests to gain death data from 2008 to 2019 in the nation’s biggest jails. Today, jail by jail and state by state, it is making that information available to the public. Reuters examined every large jail in the United States, those with 750 or more inmates. And, to ensure it examined deaths across the country, it obtained data for the 10 largest jails in each state. The data covers 523 jails or jail systems.

Original Data

Acknowlegement

Foto von Hasan Almasi auf Unsplash

This project sought to investigate a possible relationship between sentencing guidelines and family structure in the United States. The research team developed three research modules that employed a variety of data sources and approaches to understand family destabilization and community distress, which cannot be observed directly. These three research modules were used to discover causal relationships between male withdrawal from productive spheres of the economy and resulting changes in the community and families. The research modules approached the issue of sentencing guidelines and family structure by studying: (1) the flow of inmates into prison (Module A), (2) the role of and issues related to sentencing reform (Module B), and family disruption in a single state (Module C). Module A utilized the Uniform Crime Reporting (UCR) Program data for 1984 and 1993 (Parts 1 and 2), the 1984 and 1993 National Correctional Reporting Program (NCRP) data (Parts 3-6), the Urban Institute's 1980 and 1990 Underclass Database (UDB) (Part 7), the 1985 and 1994 National Longitudinal Survey on Youth (NLSY) (Parts 8 and 9), and county population, social, and economic data from the Current Population Survey, County Business Patterns, and United States Vital Statistics (Parts 10-12). The focus of this module was the relationship between family instability, as measured by female-headed families, and three societal characteristics, namely underclass measures in county of residence, individual characteristics, and flows of inmates. Module B examined the effects of statewide incarceration and sentencing changes on marriage markets and family structure. Module B utilized data from the Current Population Survey for 1985 and 1994 (Part 12) and the United States Statistical Abstracts (Part 13), as well as state-level data (Parts 14 and 15) to measure the Darity-Myers sex ratio and expected welfare income. The relationship between these two factors and family structure, sentencing guidelines, and minimum sentences for drug-related crimes was then measured. Module C used data collected from inmates entering the Minnesota prison system in 1997 and 1998 (Part 16), information from the 1990 Census (Part 17), and the Minnesota Crime Survey (Part 18) to assess any connections between incarceration and family structure. Module C focused on a single state with sentencing guidelines with the goal of understanding how sentencing reforms and the impacts of the local community factors affect inmate family structure. The researchers wanted to know if the aspects of locations that lose marriageable males to prison were more important than individual inmate characteristics with respect to the probability that someone will be imprisoned and leave behind dependent children. Variables in Parts 1 and 2 document arrests by race for arson, assault, auto theft, burglary, drugs, homicide, larceny, manslaughter, rape, robbery, sexual assault, and weapons. Variables in Parts 3 and 4 document prison admissions, while variables in Parts 5 and 6 document prison releases. Variables in Part 7 include the number of households on public assistance, education and income levels of residents by race, labor force participation by race, unemployment by race, percentage of population of different races, poverty rate by race, men in the military by race, and marriage pool by race. Variables in Parts 8 and 9 include age, county, education, employment status, family income, marital status, race, residence type, sex, and state. Part 10 provides county population data. Part 11 contains two different state identifiers. Variables in Part 12 describe mortality data and welfare data. Part 13 contains data from the United States Statistical Abstracts, including welfare and poverty variables. Variables in Parts 14 and 15 include number of children, age, education, family type, gender, head of household, marital status, race, religion, and state. Variables in Part 16 cover admission date, admission type, age, county, education, language, length of sentence, marital status, military status, sentence, sex, state, and ZIP code. Part 17 contains demographic data by Minnesota ZIP code, such as age categories, race, divorces, number of children, home ownership, and unemployment. Part 18 includes Minnesota crime data as well as some demographic variables, such as race, education, and poverty ratio.

This data collection provides information on multiple prosecutions for individual offenders. The data are intended for use in the exploration and description of relationships among the various elements of the adjudication process (characteristics of the offender and offense and decisions made by various actors in the prosecution and sentencing of the offenders). The sampled incidents were drawn from two types of offenses: residential burglary and armed robbery. The collection includes only those incidents involving male offenders who were previously unknown to their victims and who were facing adjudication in adult court. The data collection instrument probed five areas for each offender and incident sampled: A. Related Incidents (information to identify all other incidents for which processing overlapped that of the sampled incident), B. Incident Description (information about the criminal incident itself, such as date and _location of the incident, date of arrest, victims, weapons, accomplices, witnesses, and evidence), C. Adjudication Process (information such as bond amount, legal representation, adjudication events and outcomes, date of sentencing, and type and length of incarceration), D. Defendant (information about the defendant himself, including date of birth, race/descent, and employment status), and E. Prior Record (information about the defendant's record, such as his age at first arrest and first incarceration, the number of times he was incarcerated, and history of drug and/or alcohol abuse).

Research shows that prison visitation is integral to the success of incarcerated people, reducing recidivism, facilitating reentry into the community, and promoting positive parent-child relationships. However, people are often incarcerated long distances from their home communities in areas that are difficult to reach by public transport, creating significant barriers to in-person visitation. Departments of corrections are exploring the use of computer-based video visits as a means to address some of the visitation needs of those in custody in a cost-effective way while continuing to encourage in-person visits. To learn more about this practice, the study team conducted the following research activities: A survey of incarcerated people: The study team surveyed 211 people incarcerated in Washington State prisons about their use of video visits, their perceptions of the service, and their experiences of in-person visits more generally. This was a self-administered, pen-and-paper survey. An impact evaluation of video visits: The study team analyzed individual-level administrative data from the Washington Department of Corrections (WADOC) and the private video visit vendor (JPay) to understand whether use of the service affected four outcomes: 1) the number of in-person visits people received, 2) the number of rule violations (of any severity) people committed in prison, 3) the number of general (ie. non-serious) rule violations they committed, and 4) the number of serious (as defined by WADOC) rule violations that were committed. The researchers used two analytic techniques: 1) a difference-in-difference test, using inverse probability of treatment weighting, and 2) Bayesian additive regression trees. An analysis of in-person visit rates: The study team analyzed administrative data relating to all people who were incarcerated for the 12 month period ending November 2015 (n=11,524). The study team produced descriptive statistics and conducted negative binomial regressions to understand the rates of in-person visits and how these related to the characteristics of the incarcerated people.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study, with assistance from the National Institute of Justice's Data Resources Program (FY2012), is a reanalysis of data from the national evaluation of the federal Serious and Violent Offender Reentry Initiative (SVORI). SVORI provided funding to 69 agencies across the United States to enhance reentry programs and coordination between corrections and community services. The national evaluation covered 16 of these sites, twelve of which provided services to the 2,054 adult ex-prisoners who are the focus of the present study. The purpose of this study is to understand whether or not offenders receive the services they say they need, and whether the degree of 'fit' between this self-reported criminogenic need and services received is related to recidivism. This study analyzes data from the SVORI multisite evaluation to assess the potential explanations for the mixed effectiveness of reentry programs. The goal is to understand whether or not service-risk/need fit is related to successful reentry outcomes, or whether the needs of returning prisoners are unrelated to their risk of recidivism regardless of how well they are addressed. For the present study researchers obtained the SVORI (ICPSR 27101) outcome evaluation datasets from the National Archive of Criminal Justice Data (NACJD). The archive holds four separate datasets from the evaluation: Adult Males Data (Part 1, N=1,697), Adult Females Data (Part 2, N=357), Juvenile Males Data (Part 3, N=337) and official recidivism and reincarceration data (Part 4, N=35,469), which can be linked on a one-to-many basis with the individual-level data in the other three datasets. To prepare the SVORI data for analysis researchers merged Datasets 1 and 2 (Adult Males and Adult Females) and created seven separate datasets containing Waves 1 through 4 survey data, National Crime Information Center (NCIC) crime data, administrative data, and sampling weights. This deposit to NACJD is intended to complement the existing SVORI dataset (ICPSR 27101). It contains an R syntax file to be used with the datasets contained in the ICPSR 27101 collection.

This dataset documents the records of mainly Black people incarcerated in the Tennessee State Penitentiary in the period directly before, during, and after the Civil War, from 1850-1870. It includes a staggering amount of formerly enslaved Civil War soldiers and veterans who had enlisted in the segregated regiments of the United States Military, the U.S.C.T. This demographic information of over 1,400 inmates incarcerated in an occupied border state allows us to examine trends, patterns, and relationships that speak to the historic ties between the US military and the TN State Penitentiary, and more broadly, the role of enslavement’s legacies in the development of punitive federal systems. Further analysis of this dataset reveals the genesis of many modern trends in incarceration and law. The dataset of this article and its historiographical implications will be of interest to scholars who study the regional dynamics of antebellum and post-Civil War prison systems, convict leasing and the development of the modern carceral state, Black resistance in the forms of fugitivity and participation in the Civil War, and pre-war era incarceration of free Black men and women and non-Black people convicted of crimes related to enslavement.

This study assessed the effects of male inmate religiosity on post-release community adjustment and investigated the circumstances under which these effects were most likely to take place. The researcher carried out this study by adding Federal Bureau of Investigation criminal history information to an existing database (Clear et al.) that studied the relationship between an inmate's religiousness and his adjustment to the correctional setting. Four types of information were used in this study. The first three types were obtained by the original research team and included an inmate values and religiousness instrument, a pre-release questionnaire, and a three-month post-release follow-up phone survey. The fourth type of information, official criminal history reports, was later added to the original dataset by the principal investigator for this study. The prisoner values survey collected information on what the respondent would do if a friend sold drugs from the cell or if inmates of his race attacked others. Respondents were also asked if they thought God was revealed in the scriptures, if they shared their faith with others, and if they took active part in religious services. Information collected from the pre-release questionnaire included whether the respondent attended group therapy, religious groups with whom he would live, types of treatment programs he would participate in after prison, employment plans, how often he would go to church, whether he would be angry more in prison or in the free world, and whether he would be more afraid of being attacked in prison or in the free world. Each inmate also described his criminal history and indicated whether he thought he was able to do things as well as most others, whether he was satisfied with himself on the whole or felt that he was a failure, whether religion was talked about in the home, how often he attended religious services, whether he had friends who were religious while growing up, whether he had friends who were religious while in prison, and how often he participated in religious inmate counseling, religious services, in-prison religious seminars, and community service projects. The three-month post-release follow-up phone survey collected information on whether the respondent was involved with a church group, if the respondent was working for pay, if the respondent and his household received public assistance, if he attended religious services since his release, with whom the respondent was living, and types of treatment programs attended. Official post-release criminal records include information on the offenses the respondent was arrested and incarcerated for, prior arrests and incarcerations, rearrests, outcomes of offenses of rearrests, follow-up period to first rearrest, prison adjustment indicator, self-esteem indicator, time served, and measurements of the respondent's level of religious belief and personal identity. Demographic variables include respondent's faith, race, marital status, education, age at first arrest and incarceration, and age at incarceration for rearrest.

This study of prison rapes used an ethnographic, culturally relativistic methodology and was conducted between April 2004 and September 2005. The study was conducted in 30 correctional institutions, 23 men's and 7 women's, in 10 states. All 23 men's institutions were the highest-security level men's prison available in each state. When women's institutions were multi-security level and housed minimum, medium, and high-security inmates, they were selected from the highest-security level housing units within the institution. A total of 564 (409 male and 155 female) inmates were interviewed. The inmates to be interviewed were selected from the general prison population using a probability sample design. Average interview length was just under an hour. The sole mode of data collection was an open-ended, semistructured inmate interview. To ensure comparability of answers, surveys were designed with each query resting on a particular concept or variable. The same interview instrument was used for both male and female inmates. Questions were asked about inmate prison history, mental health, rape, social process, domestic violence and relationships, staff, institutional factors, and perception of social roles, and demographic information. Also included are lexical responses and free list questions such as "Why do inmates have sex with other inmates?"

This pilot study was conducted in an attempt to better understand the jailed population in terms of the number of families at risk and the relationship between parental substance use and incarceration and its impact on the children of the incarcerated. The aim of the study was to describe the jailed population, their needs in relation to substance abuse and parenting issues, to explore children's risk factors resulting from having a parent with substance abuse and/or criminal justice involvement, and ultimately to offer a point of intervention for parents and children at risk. Participants included 229 men and 52 women aged 18 and older, who were in their first 48 hours of incarceration in the Santa Clara County Department of Corrections in August 2003 and who where voluntary participants in the National Institute of Justice's (NIJ) Arrestee Drug Abuse Monitoring (ADAM) Program (ARRESTEE DRUG ABUSE MONITORING (ADAM) PROGRAM IN THE UNITED STATES, 2003 [ICPSR 4020]). Male subjects were chosen through a random selection process, while female participants were taken from a convenience sample. The pilot study used a questionnaire completed as an addendum to the ADAM program main interview. Major types of variables included in this study are type and duration of alcohol/drug use, family history of incarceration, number and ages of children for whom the respondent was the primary caregiver, social consequences for the child due to the incarceration of the respondent, and if the child had any problems with drugs and/or alcohol.

ObjectiveThere is currently inconclusive evidence regarding the relationship between recidivism and mental illness. This retrospective study aimed to use rigorous machine learning methods to understand the unique predictive utility of mental illness for recidivism in a general population (i.e.; not only those with mental illness) prison sample in the United States.MethodParticipants were adult men (n = 322) and women (n = 72) who were recruited from three prisons in the Midwest region of the United States. Three model comparisons using Bayesian correlated t-tests were conducted to understand the incremental predictive utility of mental illness, substance use, and crime and demographic variables for recidivism prediction. Three classification statistical algorithms were considered while evaluating model configurations for the t-tests: elastic net logistic regression (GLMnet), k-nearest neighbors (KNN), and random forests (RF).ResultsRates of substance use disorders were particularly high in our sample (86.29%). Mental illness variables and substance use variables did not add predictive utility for recidivism prediction over and above crime and demographic variables. Exploratory analyses comparing the crime and demographic, substance use, and mental illness feature sets to null models found that only the crime and demographics model had an increased likelihood of improving recidivism prediction accuracy.ConclusionsDespite not finding a direct relationship between mental illness and recidivism, treatment of mental illness in incarcerated populations is still essential due to the high rates of mental illnesses, the legal imperative, the possibility of decreasing institutional disciplinary burden, the opportunity to increase the effectiveness of rehabilitation programs in prison, and the potential to improve meaningful outcomes beyond recidivism following release.

Characteristics of male US state prisoners with a chronic health condition (N = 643,290)*.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. The main aim of this research is to study the criminal mobility of ethnic-based organized crime groups. The project examines whether organized crime groups are able to move abroad easily and to reproduce their territorial control in a foreign country, or whether these groups, and/or individual members, start a life of crime only after their arrival in the new territories, potentially as a result of social exclusion, economic strain, culture conflict and labeling. More specifically, the aim is to examine the criminal mobility of ethnic Albanian organized crime groups involved in a range of criminal markets and operating in and around New York City, area and to study the relevance of the importation/alien conspiracy model versus the deprivation model of organized crime in relation to Albanian organized crime. There are several analytical dimensions in this study: (1) reasons for going abroad; (2) the nature of the presence abroad; (3) level of support from ethnic constituencies in the new territories; (4) importance of cultural codes; (5) organizational structure; (6) selection of criminal activities; (7) economic incentives and political infiltration. This study utilizes a mixed-methods approach with a sequential exploratory design, in which qualitative data and documents are collected and analyzed first, followed by quantitative data. Demographic variables in this collection include age, gender, birth place, immigration status, nationality, ethnicity, education, religion, and employment status. Two main data sources were employed: (1) court documents, including indictments and court transcripts related to select organized crime cases (84 court documents on 29 groups, 254 offenders); (2) in-depth, face-to-face interviews with 9 ethnic Albanian offenders currently serving prison sentences in U.S. Federal Prisons for organized crime related activities, and with 79 adult ethnic Albanian immigrants in New York, including common people, undocumented migrants, offenders, and people with good knowledge of Albanian organized crime modus operandi. Sampling for these data were conducted in five phases, the first of which involved researchers examining court documents and identifying members of 29 major ethnic Albanian organized crime groups operating in the New York area between 1975 and 2013 who were or had served sentences in the U.S. Federal Prisons for organized crime related activities. In phase two researchers conducted eight in-depth interviews with law enforcement experts working in New York or New Jersey. Phase three involved interviews with members of the Albanian diaspora and filed observations from an ethnographic study. Researchers utilized snowball and respondent driven (RDS) recruitment methods to create the sample for the diaspora dataset. The self-reported criteria for recruitment to participate in the diaspora interviews were: (1) age 18 or over; (2) of ethnic Albanian origin (foreign-born or 1st/2nd generation); and (3) living in NYC area for at least 1 year. They also visited neighborhoods identified as high concentrations of ethnic Albanian individuals and conducted an ethnographic study to locate the target population. In phase four, data for the cultural advisors able to help with the project data was collected. In the fifth and final phase, researchers gathered data for the second wave of the diaspora data, and conducted interviews with offenders with ethnic Albanian immigrants with knowledge of the organized crime situation in New York City area. Researchers also approached about twenty organized crime figures currently serving a prison sentence, and were able to conduct 9 in-depth interviews.