Bibliographic record
Abstract
Community-based participatory research (CBPR) within Indigenous communities aims to share project responsibilities and benefits equitably among community members and researchers. CBPR relies on authentic relationships that take time to build; so how does a student from a colonial institution such as Queen’s University build the necessary relationships? As a Kanien'kehá:ka (Mohawk) student about to begin my graduate degree focusing on health promotion through a CBPR partnership with Indigenous communities, I will share my story and background of disconnect, as well as the identity-struggles I had prior to deciding that this field of research was right for me. Through this presentation, I will discuss the upfront process of being involved with Indigenous research as an Indigenous student, an advocate, and an ally. Regardless of Indigenous status, examining the research process in the context of individual positionality and researcher self-awareness is critical to successful CBPR research. My goal is to provide both Indigenous and non-Indigenous research trainees with important insight about positionality, identity, power, and relationship-building as vital components of community-engaged research. I will discuss how the principles of CBPR align with Indigenous ideals and how these can be leveraged to establish connections that can support meaningful research with 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 | 0.064 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.024 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".