Can we move beyond vaccine apartheid? Examining the determinants of the COVID-19 vaccine gap
Bibliographic record
Abstract
While global health leaders call disparities in access to COVID-19 vaccines an 'apartheid,' this gap is not the first such disparity. The recurrence of these gaps in low and middle-income countries and especially in Africa, raises questions about their determinants and about the persistent failures of global health institutions to remediate them. We interrogate these determinants and questions by examining: (1) the distribution of COVID-19 vaccines; (2) primary determinants of vaccine access including availability and affordability; (3) factors affecting availability (hoarding, COVAX, and manufacturing capacity); and (4) factors affecting affordability (pricing, intellectual property rights (IPR), the TRIPS waiver and a potential pandemic treaty). We conclude that IPR constrained the affordability and availability of COVID-19 vaccines in ways inadequately addressed by COVAX and a waiver compromise thwarted by political, corporate, and philanthropic interests. While stronger limits to IPR in a pandemic treaty and a reformed International Health Regulations will not resolve structural inequities, they could meaningfully expand LMIC autonomy to protect public health. We urge equity-seeking Global South and North actors to fight for such IPR reforms as small and meaningful steps towards a more equitable global health order. Otherwise, criminally racist 'apartheids' will continue to be the norm when it comes to the distribution of essential health goods during global health emergencies.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".