‘Our project, your problem?’ A case study of the WHO’s mRNA technology transfer programme in South Africa
Bibliographic record
Abstract
In June 2021 the World Health Organization (WHO) and the Medicines Patent Pool (MPP) launched an mRNA technology transfer programme. With a South African consortium serving as the hub, the programme aimed to increase vaccine manufacturing capacity in low- and middle-income countries (LMICs) in view of the "vaccine apartheid" that was observed during COVID-19. Following Clarke's "situational analysis," the present study assessed whether the mRNA programme differs from the approach and practices that comprise current biopharmaceutical production. Numerous documentary sources, including legal agreements underpinning the programme, funding agreements, and patent filings, were reviewed. Semi-structured interviews with 35 individuals, ranging from the programme's architects and university scientists to representatives from LMIC vaccine manufacturers taking part in the programme were also conducted. While the mRNA programme may improve the sharing of knowledge, other design features, in particular, weak conditionalities around product affordability, participants' freedom to contract with third parties, and acceptance of market-based competition, are in line with the status quo. Further, WHO and MPP's tight control over the programme evokes the dynamics that are often in play in global health, to the detriment of empowering LMIC-based manufacturers to generate mRNA products in response to local health needs.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.018 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".