Vaccine inequity-induced COVID-19 dilemma: Time to sober up (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED: The ongoing coronavirus disease 2019 pandemic has not only posed a serious threat to public health but has also imposed a heavy burden on medical systems and global economies. To combat this challenge, unprecedented efforts have been made by governments and the scientific community in the development and production of vaccines. As a result, less than a year elapsed between identification of a novel pathogen sequence and large-scale vaccine rollout. However, much of the focus and debate has increasingly shifted to the looming risk of global vaccine inequity and whether we could do more to modify this risk. In this paper, we first outline the scope of inequitable vaccine distribution and identify its truly catastrophic consequences. Then, from the perspectives of political will, free markets and profit-driven enterprises based on patent and intellectual property protection, we analyze in-depth the root causes why this phenomenon is so difficult to combat. Apart from these, some specific and crucial solutions that should be undertaken in the long term were also put forward, in order to provide a useful reference for the authorities, stakeholders and researchers involved in addressing this global crisis and the next one.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".