Developing Indigenous Cultural Safety in a Post-Secondary Context
Bibliographic record
Abstract
Abstract\nAnti-Indigenous racism has become entrenched throughout Canada’s higher education system. Anti-Indigenous racism is most commonly evident in higher education in the form of covert systemic organizational practices and policies, and to a lesser degree it emerges as overt individual racism. The barriers and obstacles that systemic racism presents in higher education, combined with the intergenerational impacts of colonization on Indigenous communities, has resulted in a system where Indigenous students are less likely to transition to post-secondary education and less likely to persist towards credential completion. The purpose of this Organizational Improvement Plan (OIP) is to identify transformative strategies which can be implemented to increase Indigenous cultural safety and decrease the harm caused by anti-Indigenous racism within the Faculty of Health and Human Services (FHHS) at a rural BC college. This OIP is framed by the tenets of Critical Race Theory (CRT), Indigenous Cultural Safety, and transformative learning theory. This OIP recommends utilizing both transformative and adaptive leadership approaches to address this complex, adaptive, Problem of Practice (PoP), as both of these leadership approaches are both consistent with CRT principles. Furthermore, this OIP recommends that the first 24 months be focused on increasing knowledge for FHHS member regarding how Indigenous Cultural Safety can be enacted to build the confidence and racial stamina of faculty and staff to engage in meaningful conversations about race, racism, and white privilege both inside and outside of the classroom.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.022 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".