Under-serving the Over-represented: Indigenous Peoples' Access to Specialized Courts
Bibliographic record
Abstract
This thesis examines specialized courts (such as drug courts and mental health courts) across what is known as Canada and how they meet the needs of Indigenous persons.This thesis explores such courts' admission and participation policies and connects under an Indigenizing framework developed from four Indigenous scholars.Then, it explores the experiences of Indigenous persons with previous drug-related criminal convictions and the experiences of service providers whose roles support those clients.In general, Indigenous persons are disproportionately excluded from participation in specialized courts.I argue current specialized court policies fail to adequately account for colonial challenges and barriers faced by Indigenous peoples.I suggest recommendations that acknowledge the need for programs to return to Indigenous communities, allow for Indigenous autonomy and self-governance of such programs, Indigenous development in programs, and increased capacity to individualize approaches to the participant's needs. Chapter 1: About Me and Context"We go to the schools and they leach the dreams from where our ancestors hid them, in the honeycombs of slushy marrow buried in our bones.And us?Well, we join our ancestors, hoping we left enough dreams behind for the next generation to stumble across."-Cherie Dimaline, The Marrow Thieves
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 | 0.002 | 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 | 0.017 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".