Multiple factors shape technology transfer for the development and manufacture of vaccines in Latin America and the Caribbean
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The COVID-19 pandemic highlighted significant inequalities in access to medicines and emergency supplies, including vaccines, that persist in Latin America and the Caribbean. From a regional perspective, it is necessary to improve the conditions to ensure more equitable and inclusive access to health technologies, both in normal scenarios and during future biological threats. Technology Transfer emerges as an effective tool to permanently avoid scarcity in global and regional vaccine supplies. Here we describe the global and regional ecosystem of Technology Transfer, its actors, roles, interactions, and evolution through research of publicly available documents and interviews with experts from the region and international institutions. Additionally, we identify and analyze vaccine projects, characterize typologies of projects in the region, suggest an evolution of three temporal phases, reveal lessons from the COVID-19 pandemic and identify four drivers that expedite vaccine Technology Transfer in Latin America and the Caribbean. These drivers include (i) strengthening of regulatory capacities for vaccines; (ii) adoption of trade standards; (iii) increasing manufacture capacity, R&D, and human resources; and (iv) consideration of aggregated demand. Finally, we present recommendations to maximize the potential of scientific-technological and vaccine production capacities in Latin American and the Caribbean. They relate to the four drivers, the promotion of complementary industries, data access and availability policies, inter-institutional dialogue and coordination, public health considerations, and future work in areas of information opacity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it