Relational Systems and the Principles of Self-Determination, Reciprocity, and Mutual Benefit: Enacting Indigenous Ways of Knowing in the Evaluation of an Indigenous Health Research Network in the Province of British Columbia, Canada
Bibliographic record
Abstract
The British Columbia Network Environment for Indigenous Health Research (BC NEIHR) is one of nine Canada-wide initiatives focused on research leadership across First Nations, Métis, and Inuit communities. When developing the BC NEIHR Environmental Evaluation Framework (EEF) the authors strove to empower Indigenous people to engage in evaluation that mirrors their cultural principles. With the aim of advancing their own evaluation, and contribute to Indigenous evaluation knowledge, the authors conducted a critical content analysis of the EEF and annual reports supported by an Indigenous evaluation literature scan. This article presents the results of that meta-analysis, in which four improvement themes where found: addressing power dynamics, using methods based on Indigenous epistemologies, redefining success metrics and indicators, and the idea of becoming rather than having done. The meta-analysis concludes that the key to Indigenous evaluation lies in relational systems thinking, self-determination, reciprocity, and mutual benefit. In this work, the authors hope to contribute to the future directions of Indigenous evaluation frameworks. This article is a contribution to the Canadian Journal of Program Evaluation Roots and Relations Collection’s Western Door – Do Good Work, with the aim of supporting and advancing culturally grounded, reciprocal, and relational Indigenous evaluation principles and processes.
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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 | 0.242 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.014 | 0.030 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".