'They Call it a Healing Lodge, but Where is the Healing?': Indigenous Women, Identity, and Incarceration Programming
Bibliographic record
Abstract
This thesis examines the impacts of state-run Indigenous programming on Indigenous women’s cultural identities post-incarceration. Despite attempts to alleviate Indigenous incarceration numbers since 1999, Indigenous women in Canada continue to be one of the fastest growing federally incarcerated populations, as their numbers have more than doubled since 2001 (OCI, 2016; Reitano, 2017; Statscan, 2017). It is projected, at its current rate that by 2030 there will be more than 6500 Indigenous women housed in a federal corrections institution (Innes, 2015; OCI, 2016; Reitano, 2017; Statscan, 2017). However, there is limited focus on the impacts the criminal justice system, incarceration, and Indigenous programming may have on their perceived identity as an Indigenous woman post-incarceration. Institutional program evaluations continue to give secondary status to the voices of those imprisoned while privileging the voices of those who are employed by Correctional Service Canada reinforcing a top-down approach. Inmates serving federal time can be housed across Canada, therefore, many Indigenous women who find themselves in these institutions may not be lodged in their traditional territories, and those who transfer to a healing lodge are transferred to the Prairies. While serving time within another First Nations territory, the Indigenous women have to partake in cultural programming that is not their own due to limited access to a diverse range of knowledge keepers and Elders. At the same time, if Indigenous women want to return to their families and communities sooner, they must engage in programming, and specifically Aboriginal programming to lower their risk status to be eligible for early release. By undertaking this research from the perspective of Indigenous women, state co-ordinated Indigenous programming can be understood through the eyes of those that have lived experience, giving voice to the silenced.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".