Healing Lodges: A Strong Predictor Of Success In Canada & Recommendations Moving Forward
Bibliographic record
Abstract
In response to the vast overrepresentation of Indigenous peoples in Canadian corrections, the Correctional Service of Canada (CSC) developed initiatives that are intended to provide Indigenous offenders with culturally appropriate services to address their unique needs and reduce the number of Indigenous offenders in corrections. There is strong statistical evidence that validate the notion that culturally specific healing programs can improve the recidivism rates of Indigenous offenders post-release. In turn, this suggests that Indigenous spiritual healing has the capacity to address risk factors and prevent high recidivism rates (Milward, 2011, p. 47). However, healing lodges lack the capacity to effectively deliver culturally appropriate programming to offenders. This systematic literature review examines relevant articles and studies that confirm the effectiveness of healing lodges on Indigenous offenders risk of recidivism. As promising healing lodge sound in terms of deterring offenders away from crime, healing lodges are under-funded, under-staffed, and lack appropriate resources to effectively administer culturally specific programs to Indigenous offenders who need it most. It is recommended that the CSC allocate government funding to support such initiatives and provide greater resources to Indigenous offenders in the system to ensure healing lodges are being utilized. It is recommended that legislature and policy makers revise the Corrections and Conditional Release Act and that the CSC provides Elders, Indigenous communities and Indigenous offenders with more freedom to effectively facilitate traditional healing methods.
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 | 0.012 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".