The ANC's Medical Trial Run: the Anti-Apartheid Medical Service in Exile, 1964-1990
Bibliographic record
Abstract
South Africa's current ruling African National Congress (ANC) government inherited arelatively well-developed healthcare system that was steeped in institutionalised racism.The apartheid era created the conditions for poor health and provided poor healthcare for black South Africans.However, little research has been done on the history of health and healthcare provision for the South Africans who were exiled by the National Party in 1960 and more specifically on the medical sector developed by the ANC for exiles in civilian settlements and military camps based in newly independent, sympathetic nationstates in southern Africa.The exiled South Africans were affected by the legacy of colonialism, exposed to the repression of apartheid and were subject to the first efforts of the ANC's medical sector and (eventual) Health Department while they were in exile.Indeed, many health professionals who filled leadership positions in the post-apartheid Department of Health were trained in exile and had been a part of the medical sector in the liberation struggle during some portion of the thirty-year period that the ANC was in exile.This medical sector formed in exile is the subject of this dissertation.The history of the ANC's medical sector in exile sheds new light on the importance of health to the international legitimacy of the ANC but also to the individuals whose lives were at risk in exile.Moreover, it begins to show that the iii Department of Health was also a product of apartheid in the sense that it emerged as a political response to the inequalities in South Africa and was forced to contend with exiles that had been damaged by the South African system.Attempts to understand the post-apartheid National Department of Health in South Africa must first contend with this history of health and healthcare in exile.The Department of History and Institute of African Studies at Carleton University have been supportive and encouraging from day one.Over the last four years, the Department of History's faculty and staff have offered moral and financial support and carved out opportunities for me to succeed and for that, I am grateful.Special thanks to Joan White for her attention to detail and genuine desire to help me to meet deadlines and June Payne at the IAS for her sense of humor, love of sushi and interest in tabata. Many thanks to my Carleton University student cohort in the History Department.Will Tait and his partner Jennifer Gosslin were incredibly hospitable and supportive.I will forever be indebted to them for all of those Sunday dinners.I was fortunate to have been in the same PhD student intake as Stuart Mackay and Meghan Lundrigan.They have been good officemates, excellent sounding boards and more importantly, great friends.I am also grateful to Matthew Moore.I benefited from his resolve to hit the gym and his down-to-earth outlook on life
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".