Transformative Community: Gathering the Untold Stories of Collaborative Research and Community Re-integration for Indigenous and non-Indigenous peoples, Post-Incarceration and Beyond
Bibliographic record
Abstract
The Canadian carceral system is purposefully designed to disconnect and isolate people. Ongoing colonialism in Canada at the intersection of carceral, social service, health and child welfare systems has resulted in the disproportionate and unjust representation of Indigenous Peoples across each stage of the penal process. Given the ongoing silencing of people who are or have been incarcerated, Participatory Action Research led by Peers with living experience of the carceral state and grounded in the wisdom of Indigenous Elders is urgently needed. In this context, a research network, called the Transformative Health & Justice Research Cluster (THJRC), based out of Vancouver, British Columbia, has formed to disrupt status quo research practices, bringing together Indigenous and non-Indigenous Peer Leaders, Elders, academics, community advocates and student trainees, and support the empowerment of people who have been incarcerated. In this reflective piece, Nicolas Crier, one of the THJRC Peer Leaders, will provide an overview of the who, what, why, when and how of the THJRC, reflecting on the impacts and strengths of the collaborative community in general, as well as within the specific context of COVID-19. Co-authors, representing diverse positionalities and perspectives within the THJRC, will weigh in when relevant.
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 | 0.053 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.073 | 0.092 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.006 | 0.034 |
| Research integrity | 0.007 | 0.018 |
| 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; 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".