Sacred Healings through Telling Story: Lessons from the Sacred Grounds
Bibliographic record
Abstract
In Canada, women—in particular, Indigenous women—comprise the fastest growing population of those who are sentenced. These trends are evidence of the continued impact of colonialism and the residential school legacy that has been well documented by scholars in varying degrees and at all levels of the Canadian criminal justice system. However, changes to address discrimination and overrepresentation have mostly resulted in changes within the current system rather than changes to the system itself. Attempts to “indigenize the white system” through training, programming, legislation, employment, and funding continue to reinforce colonialism and fail those who are Indigenous, especially women and girls. In acknowledgment of such harm, Elizabeth Fry Peel-Halton and Correctional Services Canada (CSC) collaborated with local Elder, Little Brown Bear (Ernest W. Matton), to create space where women could participate in traditional sweat lodge ceremony and healing away from correctional facilities, with the goal of providing a more authentic experience for Indigenous women and other women who are sentenced. While there are sweat lodges at both federal and provincial facilities, the Sacred Grounds are the first off-site (i.e., away from the correctional institution) space like this in Canada. This research explores the ways the Sacred Grounds possibly reduces the settler-colonial imperatives of traditional bricks and bars corrections and may encourage and support women’s stories of resilience and reconciliation.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.043 | 0.034 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| 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".