A Feminist Critical Discourse Analysis of the Experiences of Community Reintegration for Women Leaving Prison
Bibliographic record
Abstract
Women are a small, yet growing, increasingly diverse and complex group out of the overall Canadian prison population. From 2005 to 2015, the population of people in Canadian prisons rose by approximately 10% and most of this growth can be attributed to the increase of visible minorities, individuals of Indigenous descent and women in prison. Presently, more than 50% of the women are under supervision in community, thus in need of support as they attempt to socially reintegrate. \n \n\tUnfortunately, in comparison to the average Canadian, formerly incarcerated women carry a greater rate of mental health and substance abuse issues and are more likely to have a history of sexual or physical abuse. In comparison to men, women are often more vulnerable and likely to experience negative outcomes from incarceration, including continuous stigmatization while re-entering the community. Thus, women leaving prison may face a wide array of constraints to achieving a healthy lifestyle. \n \n\tThankfully, decades of research have shown that relationships hold great value in helping women achieve a sense of normalcy in their lives during their transitions from prison into community. Therefore, the purpose of this thesis was to gain an in-depth understanding of the experiences of women reintegrating into community after imprisonment. To do this, I performed a feminist critical discourse analysis (FCDA) on a data set of longitudinal transcribed interviews with six women who have experienced incarceration at the Grand Valley Institution for Women (GVI). The women took part in a community-based restorative justice program, known as Stride Circles, in the Kitchener-Waterloo area.
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 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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.018 | 0.021 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".