Supporting Indigenous Students through a Culturally Relevant Assessment Model Based on the Medicine Wheel
Bibliographic record
Abstract
We describe the development of a student assessment model based on the medicinewheel for implementation in the Child Welfare course (FCC 240) as part of the Familyand Community Counselling Program at the Native Education College (NEC), a private Aboriginal post-secondary institution in Vancouver, BC. We discuss the processof developing the model from our own social locations: Roselynn is a female Caucasianinstructor with European and Indonesian heritage; Jair is a male adult learner withMestizo/Indigenous heritage from South America; and Ashley is a female Indigenouslearner with Wet'sewet'en Carrier heritage. Drawing from theory on culturally relevant assessment, we present an assessment model that privileges students' many waysof knowing in the context of a course on child welfare. The framework for assessingstudents takes into account the institutional aims and objectives of NEC, the specificcourse goals and learning objectives of FCC 240, and supports the diverse perspectivesand experiences of the Indigenous learners who are studying to be social workers. Byemphasizing these perspectives, the students can focus on their strengths as Indigenousyouth, make their learning more meaningful, and place learning within a context thatmay be more culturally relevant.
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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 | 0.021 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".