‘Check your bus’: Approaching Multiculturalism Through Depth Education
Bibliographic record
Abstract
This article explores the concept of Depth Education as a means to engage with multiculturalism in higher education. Drawing from a graduate-level course at the University of British Columbia, the authors discuss how Depth Education contrasts with traditional mastery-based education by emphasizing diagnostic approaches over prescriptive solutions. The course aimed to develop educators' capacities to handle complex cultural and historical issues related to multiculturalism, race relations, equity, diversity, inclusion, and decolonization. Using embodied exercises, self-reflection, and guided discussions, participants navigated the intricacies of multiculturalism within the Canadian context. The article critiques the limitations of multiculturalism as it is traditionally understood in Canada, highlighting its role in perpetuating colonial narratives and systemic racism. Through various pedagogical activities, including Systems Theatre and Radical Tenderness, students developed psycho-affective stamina to hold space for discomfort and complexity. The authors argue that Depth Education offers a responsible and sustainable approach to address the volatile, uncertain, complex, and ambiguous nature of contemporary social issues.
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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 | 0.004 | 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 | 0.007 | 0.020 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".