Advancing Indigenous-Led Research through Collaborative Grant Writing: A Case Study of the 2025 Cultural Safety Retreat
Bibliographic record
Abstract
The overrepresentation of Indigenous people in Canada’s criminal justice system is regarded as one of Canada’s most pressing human rights challenges. In 2021, Indigenous people accounted for 32% of all individuals in custody, up from 23% in 2012. Similar figures exist in in Aotearoa New Zealand, where Māori individuals make up 52.6% of the incarcerated population. These disparities reflect colonial legacies that continue to shape inequities in health, justice, and social systems. In response to calls from the Truth and Reconciliation Commission, the National Inquiry into Missing and Murdered Indigenous Women and Girls and the United Nations Declaration on the Rights of Indigenous Peoples, an international partnership was launched in 2023 to advance cultural safety through Indigenous-led research and knowledge mobilization, and to prioritize collaboration and relationship building across law, corrections, and mental health services. Our partnership spans three Canadian provinces (British Columbia, Manitoba, and Québec) and Aotearoa New Zealand by bringing together Elders and Knowledge Keepers, Indigenous trainees, community partners, individuals with lived experience of incarceration, academics, practitioners, and decision-makers. In July 2025, to advance our goals of developing culturally safe, Indigenous-led services for Indigenous populations involved in the criminal justice system, 22 members met for a three-day Grant Planning Retreat at a healing lodge to plan a national grant application. This retreat provided a unique space to to reimagine research through Indigenous leadership, ceremony, and relationships, bringing together community partners from across jurisdictions. Using a mixed-method approach of an online survey or semi-structured interviews, this Indigenous-led study aims to qualitatively 1) explore what relational, cultural, and structural conditions made the Grey Buffalo Grandfather Lodge effective for advancing Indigenous-led research and working collaboratively; 2) describe the Indigenous-led research process; and 3) identify best practices to inform future Indigenous-led research partnerships and community-engaged knowledge mobilization. This project will explore the effectiveness of the Grant Planning Retreat in promoting Indigenous-led, culturally safe research. Results will highlight Indigenous-led research processes and Indigenous methodologies grounded in meaningful relationships to inform future Indigenous-led partnerships and community-driven knowledge exchange. Aligned with Alexander’s (2024) principles of scholar-activism, knowledge mobilization strategies will effectively bridge the gap between scholarly research and community. Further, findings will be shared in ways that prioritize relational accountability and Indigenous protocols, ensuring that communities have authority in data interpretation and use (Global Indigenous Data Alliance, 2020; First Nations Information Governance, 2014).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.002 | 0.083 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.027 | 0.024 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".