From vaccine to visa apartheid, how anti-Blackness persists in global health
Bibliographic record
Abstract
Global health evolved from colonial medicine and hence deeply rooted in the white supremacy mindset [1].Anti-Blackness is an inescapable consequence.Definitions of anti-Blackness revolve around the positioning of Black people, their cultural practices and knowledge as inferior, the conscious and unconscious dehumanization and discrimination of Black bodies, a disdain for Black people and their lived experiences, the disenfranchisement of Black people, but above all, a system of beliefs and practices that erode their humanity.In a recent event held in Nairobi, Kenya, we discussed what anti-Blackness in global health means, why it matters, and what needs to be done to counter anti-Blackness in global health and development [2]. How does anti-Blackness manifest itself?No continent has been more impacted by the ravages of colonialism and racism than the African continent.Sadly, even today, Africans are at the receiving end of discrimination, from vaccine apartheid to visa apartheid.The Covid-19 pandemic offers a stunning recent example of anti-Blackness.No continent is less vaccinated and boosted than the African continent [3].While wealthy nations rushed to clean up the shelves, hoard vaccines, and even throw away millions of expired vaccines, the African region was left last in the line.Despite the efforts of activists and the support of most countries, a few rich countries blocked the TRIPS waiver that could have significantly expanded vaccine manufacturing in the Global South.Two years after vaccination began in wealthy nations, and even as second and third booster shots are being offered in the Global North, barely one in four people in the African region are vaccinated with two doses (as of January 2023) [3].The African region has also had the lowest Covid-19 testing rate, and access to anti-viral medications such as Paxlovid is practically non-existent.This pattern of discrimination is not new.More than 30 years ago, when anti-retrovirals (ARV) became available, they were considered too expensive to roll-out in the African region.As late as 2001, some experts maintained that ARV treatment in Sub-Saharan Africa was impossible.It took incredible activism, legal action, and community effort before they started becoming available, by which time millions of Africans got infected and died.When the Ebola outbreak ravaged West Africa during 2014-16, it killed more than 11,000 people in Guinea, Liberia, and Sierra Leone.Even intravenous hydration was seen as being too challenging during this crisis.While an overwhelming majority of the mostly White American and European healthcare workers who contracted Ebola survived, the infection killed two-
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".