Ready or not: employment, re-entry and the lasting effects of stigma after incarceration

In this thesis I explore the how former prisoners experience the transition from incarceration to employment. Employment has been identified by researchers as an essential element in exprisoners’ community re-entry process. However, the path to attaining employment after incarceration, particularly meaningful employment, remains complicated. Drawing on in-depth, semi-structured longitudinal interviews with 24 parolees occurring over a three-year period, I seek to better understand the experiences of ex-prisoners as they attempt to find work. I aim to understand whether individuals are prepared to pursue employment immediately upon release from prison and the factors that impact their readiness, or lack thereof. Upon recognizing that individuals in the study tended to identify themselves as not ready for employment, I sought to understand why they were still expected to begin working using Goffman’s (1963) theory of stigma. I suggest that in many cases, attempting to manage one’s stigmatized status slows individuals’ return to work. As well, I suggest that the stigma associated with time spent incarcerated undermines individual credibility, and for this reason, participants’ assertions that they do not feel ready to begin working are often not accepted

Appendix B. Markov chain Monte Carlo algorithm.

Markov chain Monte Carlo algorithm

Appendix A. Prior specifications, prior sensitivity, and goodness of fit for the harbor seal example.

Prior specifications, prior sensitivity, and goodness of fit for the harbor seal example

Supplement 1. Source code and example data for implementing the Markov chain Monte Carlo algorithm.

<h2>File List</h2><div> <p><a href="MCMCalgorithm.r">MCMCalgorithm.r</a> -- (md5: 66b96318b9d549b5abd7a98178ccc6c8)</p> <p><a href="MCMCalgorithm.c">MCMCalgorithm.c</a> -- (md5: c637bb779c8f04eaf247974a6409ef1b)</p> <p><a href="MCMCalgorithm.dll">MCMCalgorithm.dll</a> -- (md5: 479e438af5b72bb05d8a413b487f8d2e)</p> <p><a href="data.RData">data.RData</a> -- (md5: 25eccac47d5082a651ec6d1040daf939)</p> <p><a href="Initial_values.RData">Initial_values.RData</a> -- (md5: 8da04b2b834f00fe2f7d8669d34e7550)</p> </div><h2>Description</h2><div> <p>MCMCalgorithm.r contains R code for loading the data (data.RData and Initial_values.RData), data pre- and post-processing, loading the dynamic link library file (MCMCalgorithm.dll), and calling the .C function for interfacing the compiled C code (MCMCalgorithm.c) with R (on a machine running Windows). </p> <p>MCMCalgorithm.c contains C code for implementing the MCMC algorithm.</p> <p>MCMCalgorithm.dll is a dynamic link library file containing the compiled C code.</p> <p>data.RData contains the data for 17 harbor seals.</p> <p>Initial_values.RData contains starting values for initialization of the MCMC chain.</p> </div

Appendix C. Posterior summaries for the harbor seal example.

Posterior summaries for the harbor seal example

Fin whale #80704 tracks obtained from LS (black) and KF modelled (red) locations.

<p>Estimated locations (circles) and tracks (lines) of fin whale #89969 obtained from fitting a switching state-space model to Least Squares (LS) (black) and full Kalman filtered (KF) (red) data. The 95% probability ellipses of locations derived from the LS-based model are shown in green. A. Complete tracks showing the increase in track length resulting from the application of the KF algorithm (red). B, C. Detail of the tracks showing the majority of KF locations within the 95% probability ellipses of LS locations.</p

Fin whale #89969 tracks obtained from LS (black) and KF modelled (red) locations.

<p>Estimated locations (circles) and tracks (lines) of fin whale #89969 obtained from fitting a switching state-space model to Least Squares (LS) (black) and the full Kalman filtered (KF) (red) data. The 95% probability ellipses of locations derived from the LS-based model are shown in green. A. Complete tracks showing the increase in track length resulting from the application of the KF algorithm (red). B, C, D. Detail of the tracks showing the majority of KF locations within the 95% probability ellipses of LS locations.</p

pv42_data

pv42_dat

Trip-averaged error in locations estimated from LS and KF models relative to Argos data quality.

<p>Relationship between mean errors (±SD shown as vertical bars) in locations estimated from state-space models fit to Least Squares (LS) (black) and Kalman filtered (KF) (red) data per harbour seal trip and quality of Argos telemetry data used to fit the models: A–B. Number of locations. C–D. Time step (h) between locations. E–F. Proportion of locations of LC 0-B. Different trips from the same seal have the same symbol.</p

Differences in locations estimated from KF and LS models for all fin whales.

<p>Differences in locations estimated from switching state-space models fit to Kalman filtered (KF) (red dots) data are plotted as offsets from locations calculated from the same models fit to Least Squares (LS) data. Standard ellipses were fitted to 95% of KF data points. A. Fin whales #80702 (red), #80704 (blue) and #80707 (green). B. Fin whales #80713 (black), #89969 (orange). C. Fin whale #80716 (pink).</p