The violence of curriculum: Dismantling systemic racism, colonisation and indigenous erasure within medical education
Bibliographic record
Abstract
BACKGROUND: Epistemic violence is enacted in medical curricula in mundane ways all the time, negatively impacting learners, teachers and patients. In this article, we address three forms of such violence: White supremacy, indigenous erasure and heteronormativity. METHODS: In this article, we examine the knowledge systems of medicine as a global phenomenon, impacted by Western and European ideologies of race and colonisation, both produced by them, helping to reproduce them through authoritative and hegemonic ideologies. We seek not only to problematise but also to propose alternative teaching approaches rooted in the Global South and in Indigenous ways of knowing. Taking inspiration from Paulo Freire, we advocate for the development of critical consciousness through the integration of critical pedagogies of love, emancipation and shared humanity. Drawing on Irihapeti Ramsden, we advocate for cultural safety, which emphasises power relations and historical trauma in the clinical encounter and calls for a rights-based approach in medical education. Deliberately holding space for our own vulnerabilities and that of our students requires what Megan Boler calls a pedagogy of discomfort. CONCLUSIONS AND SIGNIFICANCE: Our perspectives converge on the importance of critical consciousness development for culturally safe practice in medical education, acknowledging the need to emphasise a curriculum of shared humanity, introducing the concept of Ubuntu from Southern Africa. Ubuntu can be encapsulated in the phrase 'I am because we are', and it promotes a collective approach to medical education in which there is active solidarity between the profession and the diverse populations which it serves.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.046 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.018 |
| 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".