Negotiating and Exploring Relationships in Métis Community-Based Research
Bibliographic record
Abstract
Adding a Métis voice to the larger discourse on Indigenous (Métis, First Nation, and Inuit) health research, this work shares experiences and insights gained in relationship building from a community-based Métis research project entitled, Converging Methods and Tools: A Métis Group Model Building Project on Tuberculosis. A collaborative partnership between PhD student Amanda LaVallee, the Métis Nation – Saskatchewan (MN-S) Health Department and two independent health researchers, the project, conducted from 2010 to 2012, incorporated a System Dynamics participatory methodology called Group Model Building (GMB), with Métis research methods, ethics, and knowledge, to build a model of tuberculosis (TB) experience in Saskatchewan Métis communities. This article examines the co-author’s experiences with these collaborative methodologies and with the other partners in the research project, as well as the relational research stories that were essential to the practice of Metis community-based research. Moving beyond discussion of objectivity toward transparency about our presence within the research relationship, this work offers our collaborative experience as a success, and provides inspiration and insight on how to engage in ethical, competent, culturally appropriate, and relevant community-based research.
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 | 0.066 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.030 | 0.051 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".