Continuing the Evaluation Journey: Sharing the Lunaape Seven Directions Medicine Wheel (7DMW™) Model
Bibliographic record
Abstract
As part of decades of Indigenous evaluation scholarship in the global North and South this article highlights how decolonized, culturally responsive, and Indigenous evaluation frameworks, theories, and methods can be developed and refined over time. Using a Lunaape Medicine Wheel framework, the author builds on the four directions and extends it to a Seven Directions Lunaape Medicine Wheel (7DMW™) model using traditional teachings and language, educational and career pathways development, and real-world applications. The article offers multiple examples of how the 7DMW framework has been used in contemporary ways to evaluate entrepreneurial business values, ethics, philosophy, and operations an has been applied to evaluation projects and initiatives. The article concludes with reflections on capacity building and strategies for bridging Indigenous and non-Indigenous contexts and evaluators. The reader should leave with professional, academic, and personal insights that should be considered for future evaluative thinking, professional development, equitable and sustainable resource allocation, policy, and practice at the program, organizational, systems, and nation-to-nation (First Nation and public government) levels.
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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 | 0.184 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.022 | 0.072 |
| Scholarly communication | 0.040 | 0.034 |
| Open science | 0.005 | 0.036 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".