Access to Justice in Indigenous Communities: An Intercultural Strategy to Improve Access to Justice
Bibliographic record
Abstract
In 2015, the Canadian Research Institute for Law and the Family partnered with Calgary Legal Guidance (CLG) to develop a project that would build intercultural partnerships in two Indigenous communities in order to build agency capacity that would increase access to justice. The project was funded in large part by the Human Rights and Education Multiculturalism Fund, with in-kind funding provided by the Institute through the Alberta Law Foundation, CLG and our community partners. Our community partners included Vanessa Omeasso of the Restorative Justice program in Maskwacis and Dr. Laura Kiepal of the Peace River Region Women’s Shelter in Peace River. In addition to agency partners and community members, we have also worked with Elders from these communities who have advised on the strategy. The project goal was to improve access to the justice system for all people, particularly in communities where there are barriers such as transportation and a lack of legal education. Over the last year, we conducted stakeholder meetings with community members, justice staff and social agency staff, and compiled information about the work already being undertaken in the communities that have been working with us. Our project sought to meet five primary outcomes: 1. establish intercultural partnerships that meet the diverse and unique needs of Indigenous communities; 2. foster agency relationships in order to deliver the project strategy, developed by the community and elder consultations; 3. support strategic partnerships to meet future research and project needs, identified throughout the consultations and focus groups; 4. increase access to and utilization of existing legal services in Indigenous communities; and 5. generate a method of practice for working inclusively within Indigenous communities.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.030 | 0.010 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.034 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".