On the Ethics of Vaccine Nationalism: The Case for the Fair Priority for Residents Framework
Bibliographic record
Abstract
COVID-19 vaccines are likely to be scarce for years to come. Many countries, from India to the U.K., have demonstrated vaccine nationalism. What are the ethical limits to this vaccine nationalism? Neither extreme nationalism nor extreme cosmopolitanism is ethically justifiable. Instead, we propose the fair priority for residents (FPR) framework, in which governments can retain COVID-19 vaccine doses for their residents only to the extent that they are needed to maintain a noncrisis level of mortality while they are implementing reasonable public health interventions. Practically, a noncrisis level of mortality is that experienced during a bad influenza season, which society considers an acceptable background risk. Governments take action to limit mortality from influenza, but there is no emergency that includes severe lockdowns. This "flu-risk standard" is a nonarbitrary and generally accepted heuristic. Mortality above the flu-risk standard justifies greater governmental interventions, including retaining vaccines for a country's own citizens over global need. The precise level of vaccination needed to meet the flu-risk standard will depend upon empirical factors related to the pandemic. This links the ethical principles to the scientific data emerging from the emergency. Thus, the FPR framework recognizes that governments should prioritize procuring vaccines for their country when doing so is necessary to reduce mortality to noncrisis flu-like levels. But after that, a government is obligated to do its part to share vaccines to reduce risks of mortality for people in other countries. We consider and reject objections to the FPR framework based on a country: (1) having developed a vaccine, (2) raising taxes to pay for vaccine research and purchase, (3) wanting to eliminate economic and social burdens, and (4) being ineffective in combating COVID-19 through public health interventions.
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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 | 0.099 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.104 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.024 | 0.030 |
| Insufficient payload (model declined to judge) | 0.004 | 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".