THE ANTECEDENTS OF RESPECT IN AN INMATE NETWORK
JACOB YOUNG (ASU)
Derek Kreager (PSU) David Schaefer (ASU)
Gary Zajac (PSU) Martin Bouchard (SFU)
Dana Haynie (OSU)
Funded by the National Science Foundation (Award#1457193)
American Society of Criminology Annual Meeting
18 Nov 2015
¨ Gary Zajac, PSU Justice Center for Research
¨ David Schaefer, ASU
¨ Derek Kreager, PSU
¨ Sara Wakefield, Rutgers
¨ Dana Haynie, OSU
¨ Martin Bouchard, Simon Fraser
¨ Michaela Soyer, Hunter College
¨ Bret Bucklen, PA Dept. of Corrections
The research team
¨ Marin Wenger, PSU Criminology
¨ Robert Hutchison, PSU Criminology
¨ Corey Whichard, PSU Criminology
¨ Wade Jacobsen, PSU Criminology
¨ Kim Davidson, PSU Criminology
¨ Gerardo Cuevas, PSU Criminology
¨ Edward Hayes, PSU Justice Center
Graduate researchers
Reopening the black box
“Any effort to reform the prison – and thus to reform the criminal—which ignores [the] social system of the prison is as futile as the labors of Sisyphus”
Gresham Sykes, 1958
Prison Social System?
Consequences of Incarceration
Causes of Crime and Incarceration
¨ Purpose: A contemporary social network study of a prison unit. ¤ Understand prison as a social space.
¤ How does network position in prison and network connections to the outside relate to success or failure while in prison and after released?
¤ Focus of this presentation is on the relationship between individual characteristics and respect.
The PINS project
Prison Social System
¨ How are prison social systems organized?
¤ Deprivation Perspective and the “Pains of Imprisonment” n Those who alleviate the impact of structurally-generated
alienation are afforded increased status
¤ Importation Perspective n Informal prison structure reflects regional, racial, and ethnic
divisions
Network Approach
¨ Relational Mechanisms and Network Structure
¤ Network Popularity n Deprivation: Experience, an “old head” n Importation: Criminal Capital, “street cred”
¤ Homophily n Importation: Race, Age, Geography
Data & Approach
The PINS project
¨ Data
¤ Self-report instrument n Measures of health, visitation, experience, etc.
¤ Longitudinal design n Wave 2 has been collected!
¤ Combines survey data with DOC data on background, offense history, misconduct, visitation etc.
Data collection
¨ Location: “Medium-Security Midwestern Prison” ¤ Focus on one block
¤ Includes inmates with different offenses (very violent to drug, sexual, and property offenses)
¤ No misconduct for 3 months prior to admittance to this block
¤ Inmates have more freedom to move around and interact with one another than in general population.
Data collection
¨ Security Level 2 unit (N=205 inmates) ¤ Unit separated from other prison units (clear network boundary)
¤ Greater freedom of association ¤ Many parole eligible (also high turnover)
¨ Wave 1 survey administration, July 2015 ¤ 4 interviewers over 3 weeks
¨ Computer-Assisted Personal Interviews (CAPI) ¤ Laptop computers and interviewers sat beside inmates
¤ 142 inmates (70% of unit) participated
Operationalizing “Respect”
¨ 1. Network Measure
¤ Roster Method: Respondents were asked to indicate which inmate were the “most powerful and influential” in the prison.
¤ Unlimited nominations and up to 3 write-ins. n Max 11 outdegree nominations n Mean outdegree = 0.785
Operationalizing “Respect”
¨ 2. Follow-up Qualitative Measure
¤ After the network nominations, respondents were asked to say why they identified a particular individual as “powerful/influential”.
¤ This information was provided for three most powerful/influential nominations.
Power/Influence Network
142 (93%) provided nomination data (white) and 10 (7%) were nominated, but did not take the survey (black)
Power/Influence Network (isolates excluded)
Basic Properties Lots of Isolates 152 nodes, 52 (34%) are isolates Low Density Density = 0.016 Reciprocity = 0.025 Few Nominations Mean Indegree = 1.61 Node size proportional to indegree
Operationalizing Deprivation and Importation ¨ Provided by DOC and linked to respondents
¨ Deprivation Hypothesis ¤ More ties (i.e. higher indegree) for those with more time in
prison, older, and life sentence
¨ Importation Hypothesis ¤ More ties (i.e. higher indegree) for those with higher offense
gravity score, in gang/STG, and co-offenders ¤ Homophilous ties for race, and state/city (geography)
Exponential Random Graph Models
¨ Provide a model of how a network is structured. ¤ We have an observed network, we are postulating the
stochastic processes that generated that network.
¨ Expresses the probability of observing a tie between i and j given some term (network configuration)
¨ We are interested in actor attribute models.
Exponential Random Graph Models
¨ Actor Attributes and Power/Influence Network
¤ Network Popularity n Is the probability of a tie from i to j more/less likely given j’s
attribute?
¤ Homophily n Is the probability of a tie from i to j more/less likely if i and
j’s have the same/similar attribute?
Exponential Random Graph Models
¨ Control for: ¤ “get along with” nominations
n Indegree and identification
¤ Self-organizing processes n Mutuality (iàj and jài), transitivity (iàjàk and iàk)
¤ outdegree restriction
Results
Results: Deprivation and Importation
¨ Individuals were more likely to receive ties: ¤ The more time in prison ¤ Older ¤ Being in gang or STG
¨ Homophily observed for: ¤ Being from the same state ¤ Whites
¨ Other effects: ¤ Bias against reciprocity; bias for transitivity ¤ Those with power/influence also “get along with” others
Power/Influence Network: Size proportional to “Years in Prison” Color corresponds to race (Orange Hispanic) Shape corresponds to gang/STG (triangle is gang/ STG)
Power/Influence Network: Size proportional to “Years in Prison” Color corresponds to race (Orange Hispanic) Shape corresponds to gang/STG (triangle is gang/ STG)
Power/Influence Network: Size proportional to “Years in Prison” Color corresponds to state Shape corresponds to gang/STG (triangle is gang/ STG)
Simulating from the model produces a network that resembles the observed network. Suggests that the network configurations proposed in the model are reproducing the network.
Results: Qualitative Data
¨ Qualitative data supports the findings of the ERG models
¤ Examples: n “He’s got time in”, “Respected by inmates and guards”
“Doesn’t care about black and white thing” n “Older guy, he’s been here for awhile”, “Has a lot of
insight”, “Been in since he was 14”, “He stops fights” n “Lifer, knows how to carry himself”, “Very humble”, “Gets
along with COs”, “Lots of time in unit”
¨ Strong evidence of an “old head” status system
Summary and Conclusions
Summary
¨ Some evidence for both the deprivation and importation arguments about how respect is organized in a prison.
¨ Qualitative data support the quantitative approach, but additional insights are gleaned by a network perspective.
Next Steps
¨ Examine deprivation and importation indicators from the PINS survey instrument.
¨ Utilize longitudinal data to observe change in status for inmates and integration of new inmates.
