Global vaccine equity? Reflections, lessons, and a way forward
Bibliographic record
Abstract
Injustice anywhere is a threat to justice everywhere.We are caught in an inescapable network of mutuality, tied in a single garment of destiny.Whatever affects one directly, affects all indirectly."-Martin Luther King Jr In his keynote address at the 75th World Health Assembly, Dr. Tedros Adhanom Ghebreyesus, Director-General of the World Health Organization (WHO), called for a paradigm shift in global health.He spoke of the need to focus on a more comprehensive approach that includes prevention, promotion, and protection of health [1].The COVID-19 pandemic exposed and exacerbated how vulnerable and unprepared our health systems are, and demonstrated how far we are from achieving equity in response and recovery.In order to tackle the root causes of inequity and reorient our health systems, a holistic approach to global health will be critical.It is also important to note that we already have the capacity, technology, knowledge, human power, and financing required to end this pandemic and the inequalities associated with it.However, due to resistance bourne out of capitalist approaches, geopolitical power plays, and outsized corporate interests that dictate pandemic response, our inability to meaningfully ensure 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 | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.028 | 0.046 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".