Culture as Healing: A Delphi Study on Access to Culture and Justice Involvement for Indigenous Persons
Bibliographic record
Abstract
Among other factors, Indigenous peoples’ unique cultures must be considered in decisions throughout Canada’s criminal legal system, including bail, sentencing, parole, and corrections. However, several studies have found weak implementation of these obligations in practice. Existing literature speaks to the importance of culture-based interventions for Indigenous persons in many contexts and from many distinct disciplinary perspectives. However, it is unclear where consensus can be found on basic contours of the relationship between access to culture and justice involvement for Indigenous persons. Consensus is an important threshold, as it can support judges and other adjudicative decision-makers considering expertise from outside law without an expert witness. To support greater rigour and consistency in how adjudicative decision-makers in the criminal legal system approach culture-based interventions for Indigenous persons, this study used the Delphi method to determine where consensus could be identified among subject matter experts. The findings outline substantial areas of agreement amongst a group of experts that included both academic and front-line experts. The scope of consensus and the implications of these findings for the consideration of Indigenous cultures in the criminal legal system are discussed.
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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 | 0.039 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".