Anti-Indigenous Racism Training and Culturally Safe Learning: Theory, Practice, and Pedagogy
Bibliographic record
Abstract
Anti-Indigenous racism is deeply and indelibly etched into the policies and practices which inform institutions and systems across Canada. Educational “spaces” can reproduce oppressive social structures without careful and critical pedagogical consideration. One of the ways to address racism toward and impacting Indigenous people is through anti-Indigenous racism education. We use the San’yas Indigenous Cultural Safety Training program as an example of anti-Indigenous racism training. We examine the relevance and shortcomings of antiracism and critical race theories in the context of anti-Indigenous racism, and explore the manifestations of anti-Indigenous racism in adult education environments. Indigenous cultural safety (ICS) gained increasing attention when cultural and intercultural competency was identified within the Calls to Action in the report by the Truth and Reconciliation Commission of Canada as required training across sectors including health, education, justice, public services, business, and child welfare. The intent of ICS training is to ensure safe and equitable services and care, free of discrimination, to Indigenous people. Within the San’yas Indigenous Cultural Safety Training program, it was recognized that if anti-Indigenous racism educators are to effectively address racism and resistance, a set of core competencies of knowledge, self-awareness, and skills must be developed to support their work. We outline how the ICS pedagogical model was developed, and explore the ways in which Indigenous and non- Indigenous educators must confront and address personal triggers, resistance, emotionality, microaggressions, and everyday racism, and must assert Indigenous perspectives in the classroom. We also review the work that has been undertaken to research and unpack anti-Indigenous educator experiences and effective pedagogical approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.051 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| 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".