The Challenge of Vaccine Nationalism
Bibliographic record
Abstract
The COVID-19 pandemic has had a devastating impact on global health for almost two years, resulting in nearly 200 million cases and over 4 million deaths worldwide. Despite a range of non-invasive public health measures, (i.e. physical distancing, and masks) vaccines have been one of the more critical and effective interventions to slow the pandemic. Produced at record-breaking speeds, the highly efficacious mRNA vaccines represented hope for many. Including global health organizations who have called for strategies to maximize vaccine equity since their conception. While many high-income countries (HICs) agreed to prioritize global vaccine equity; in truth, individual health outweighed community health. The reality of HICs vaccine purchasing behaviors and distribution have exposed a different agenda - one that aligns with a neoliberal emphasis on individuals and profits at the expense of global good. This commentary questions the efficacy of global health agreements and the commitment from wealthy countries to address global health inequities through a one health framework. Ultimately, concluding that the path to global vaccine equity will require a commitment to global good. Vaccine nationalism and lack of equitable global health policy continues to fuel a never-ending health crisis. HICs must be held accountable for the lack of commitment to global health equity.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| 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".