“If They’re Good Girls”: A Qualitative Examination of Arts-Based Programs for Federally Sentenced Women in Canada
Bibliographic record
Abstract
Prisons are spaces of violence that strip autonomy and a sense of personal identity from prisoners. Art and arts-based programming can not only be a way to combat the monotony of prison life, but to build communication skills, increase self-esteem, and help prisoners to build better relationships with themselves and with one another. Most of the research and literature surrounding arts programs for prisoners is centered around men. This project was an opportunity to explore the types of arts programs currently available to women and contribute to filling in gaps in the literature. By analyzing documents obtained via Access to Information Requests from Correctional Services Canada and conducting one-on-one semi-structured interviews with program developers and mentors, it was discovered that there is not much in the way of arts-based programming available for women – and even less access for Indigenous women. The programs that are available leave women, their needs, and their trauma out of the discussion and are not mentioned in program guides. When women are discussed, it is done so using infantilizing and misogynistic language (i.e. “if they’re good girls”) that is deeply problematic. The programs are often funded by external sources, so funding is not guaranteed from year to year. The analysis of these documents and interviews lead to recommendations, including giving women (including Indigenous, queer, and trans women) more opportunities to run arts-based programs, more funding from CSC in order to combat unpaid labour, and openly including discussions of women and the issues that incarcerated women face in program guides and outlines.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.034 | 0.017 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".