Mapping the Journey from the Head to the Heart: Actualizing Indigenization in the Health and Human Services Education
Bibliographic record
Abstract
Camosun College’s commitment to Truth and Reconciliation includes Indigenization. At the College, Indigenization means the world views of Indigenous students are reflected in curriculum practices and non-Indigenous students are prepared to build better relationships with Indigenous peoples. The College uses a number of models to support Indigenization including an Indigenized Quadrant Model based on Wilber’s (1997) Integral theory. This Model is used in this research study to inventory the Indigenization interventions and activities internal and external to the College and examine participation rates by non-Indigenous faculty. At the time of the study, respondents viewed the College as midway on its progress of Indigenization. The results suggest that developing a few activities across the four quadrants of the Model and leveraging relevant activities in the larger community may be sufficient to build an Indigenization program within a post-secondary institute. We also identified a need for more advanced training to support non-Indigenous faculty to fully Indigenize their teaching practices. The focus of this advanced training includes developing skills to address racism and confront privilege not only in their students but also their professions. For administrators wanting to actualize Indigenization, we recommend the continued alignment, monitoring, and measurement of the impacts of Indigenization activities and programs.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".