“Study us to Life”: Reflections from an Indigenous Community-Engaged Research Workshop & the Future of University-Community Research Relationships
Bibliographic record
Abstract
Following an Indigenous community-engaged research workshop, we reflect on the efforts of graduate students to conduct, change, and create partnerships with Indigenous communities. We speak to the ways Indigenous voices must be represented in the future of university-community partnerships and how building longstanding relationships is key to rigorous research practice. Research involving Indigenous communities requires a rigorous process to ensure Indigenous voices are centered. However, prioritizing processes that ensure that Indigenous life is seen, heard, and portrayed properly is challenging for graduate students within current academic training environments. There is a critical need to address the multifaceted challenges that impact the trajectory of Indigenous research particularly in relation to the historic trauma of unethical research, and the work required by academics to (re)concile this today. We address these challenges by discussing community-engaged research approaches in university-community research partnerships, and the benefits of longstanding relationships between PIs and community partners.
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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 | 0.053 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.063 | 0.049 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.008 | 0.034 |
| Research integrity | 0.015 | 0.039 |
| Insufficient payload (model declined to judge) | 0.005 | 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".