Living After Prison:The Experience of (Re)integration to Society After Release From an Ontario Correctional Facility
Bibliographic record
Abstract
As individuals are released from incarceration in Ontario institutions they face a variety of barriers to their successful (re)integration.With the current reconviction rate at 44% within the first year of release, those being released quite clearly have trouble with this period of their lives.This study will therefore examine the (re)integration process at different stages, in order to understand how people cope with life after prison, and how their experiences of incarceration continue to affect them long after release.Understanding the ways that identity production and gender come to be affected by, and affect, time spent inside, and importantly the (re)integration process will give us a greater understanding of incarceration and life after.This will be achieved through ethnographic data gained throughout the fieldwork process, along with examinations of academic literature focused on identity, gender, and power.It is important for us to further understand (re)integration barriers and experiences, in order to help programming in the future.III Acknowledgments I would like to start by thanking my thesis supervisor Dr. Brian Given, who has provided me with guidance, great conversations, and an overall fantastic experience of anthropological education.I would also like to thank Dr. Kristin Bright who helped me to understand where I fit in the anthropological discipline, who was always there to talk to and to provide reassurance that I could write this thesis, and who provided such a wonderful atmosphere at Carleton University.Along with my committee, I would like to thank the other students in my cohort who made this school experience so much fun, and who were always there to chat and hang out when it was needed most.I would like to thank my family, Dirk Sr., Bonnie, Stephanie, and Jenifer who have always been so supportive, loving, and willing to listen and provide guidance when needed.Sometimes the research and writing process can be overwhelming, and having family who are as supportive as you all makes the difference.This thesis was also heavily impacted by my friends who kept me sane over the last two years.Hanging out with Courtney and Andrew has been such an important part of my life, and you two mean more to my well-being than I can ever express (at this point you should both truly be in my family section).Matt, you have always been such a supportive person in my life, and your longdistance phone calls are always reassuring.MacCuaig, Steve, Gui, Taylor, Sara T., Shaun, Genrys, Kaleb, Scott, you have all been just great people to have in my life, and I am so honoured to call you friends.Lastly, Shigeru Miyamoto, thank you for giving me such a great way to spend too much of my time.Finally, I would like to thank the most important person in my life, my lovely wife Sara.You have been there in the best times, the difficult times, and throughout the mundane times.It is so fantastic that I am able to spend my life with my best friend, who always knows how to make me laugh, and who is never afraid to say what is on her mind.I know I'm the emotional one of the two of us, and so this I why I use this space to tell you that you mean more to me than I can
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".