Indigenizing our Research
Bibliographic record
Abstract
The use of data intensive health research has allowed for greater understandings of population health. When conducting data intensive health research, engaging and involving the community is essential for conducting meaningful research that is responsive to the public’s needs. Particularly, when engaging Indigenous communities in research, there is a need to understand historical and ongoing impacts of colonialism and recognize the strengths in Indigenous Peoples’ knowledges and experiences while supporting Indigenous leadership and self-determination in research. This article describes the approach our research team/organization used to engage and involve Indigenous people living with HIV in three research projects using large, linked datasets and looking at HIV outcomes of Indigenous populations in Canada. The foundation of these projects was simultaneously: 1) supporting Indigenous people living with HIV to be involved as research team members, 2) developing research questions to answer with available datasets, and 3) integrating Indigenous and Western ways of knowing. We have identified important considerations and suggestions for engaging and involving Indigenous communities and individuals in the generation of research ideas and analysis of linked data using community-based participatory research approaches through our work. These include engaging stakeholders at the start of the project and involving them throughout the research process, honouring Indigenous ways of knowing, the land, and local protocols and traditions, prioritizing Indigenous voices, promoting co-learning and building capacity, and focusing on developing longitudinal relationships. We describe keys to success and learnings that emerged. Importantly, the methodology practiced and presented in this manuscript is not a qualitative study design whereby research subjects are surveyed about their experiences or beliefs. Rather, the study approach described herein is about engaging people with living experience to co-lead as researchers. Our approach supported Indigenous people to share research that addresses their research priorities and responds to issues relevant to Indigenous Peoples and communities.
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 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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".