The Letting Die of the South Asian Body: a Foucauldian Analysis of White Hegemony in Western Cardiovascular Medicine
Bibliographic record
Abstract
Cardiovascular disease is the second leading cause of death in Canada and disproportionately affects those of South Asian ancestry. Anecdotally, stories of missed signs and emergency bypass surgeries are abound; empirically, medical research has identified a series of distinct risk factors for South Asian individuals. However, these factors are typically unrecognized by healthcare workers who are typically trained to use recommendations that are founded research done using Caucasian participants. The consequence of this omission is the normalization of the Caucasian body as 'the body' in medicine through disciplinary and regulatory mechanisms, and the 'letting die' of the South Asian body as a result. In taking a Foucauldian approach to this issue, this essay first maps the empirical evidence for the heightened CVD risk in South Asians, namely their predisposition to developing type 2 diabetes mellitus, metabolic syndrome, and dyslipidemia, among other factors. Disciplinary mechanisms to enforce social cohesion discount these differences as exceptions, and attempt to rehabilitate the South Asian body towards the Caucasian norm. These actions are often subconscious, but result in real actions like spending less time with South Asian patients, misuse of assessment metrics, and lower cardiac rehabilitation referral rates. On a population level, research funding is rarely given to studies investigating disease in particular ethnic groups. Hence, clinical practice guidelines must rely on incomplete data to create population-level recommendations. These guidelines act as if they apply to all individuals, but are in fact partisan; thus, the biopolitical control of populations is made apparent through the racist undertones that thrum beneath the veneer of an equal society. Ultimately, this essay serves a counterhistorical function, and demands recognition of the South Asian body in the medical literature. The current medical regime routinely discounts populations who exist outside the norm. Future research and acknowledgement of these groups is necessary to ensure equitable treatment of all patients, regardless of their background.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".