Invitations to dignity and well-being: cultural safety through Indigenous pedagogy, witnessing and giving back!
Bibliographic record
Abstract
In this article, three Métis authors, engaged in human service, share their conceptualization of cultural safety in educational settings. Their examples pertain more specifically to learning moments where Indigenous pedagogy is used to convey aspects of the colonial history and various forms of violence towards Indigenous peoples in Canada. In cases where there is a diverse or multicultural learning group, housed within a dominant Euro-Canadian culture, cultural safety can be designed to create a learning environment that promotes increased trust, sharing and exploration of “risky subjects”. This article is structured around a presentation of a pedagogy developed by Jeannine Carriere and Cathy Richardson in an Indigenous cultural sensitization training for child and youth mental health practitioners in British Columbia. Their approach encircles first-person testimony shared by Vicky Boldo, provides a structure for witnessing such testimony and then invites feedback from Vicky in relation to cultural safety for those who educate from Indigenous perspectives. The authors address the issue of backlash and White guilt that are often evoked when truths about violent histories are brought to the fore.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| 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".