Flashing red lights: the global implications of COVID-19 vaccination passports
Bibliographic record
Abstract
### Summary As COVID-19 vaccination roll-outs are progressing in wealthy parts of the world, several countries are implementing or investigating COVID-19 vaccination passports that restrict access to restaurants, sports events or universities to those who have been fully vaccinated against COVID-19. While these discussions focus on domestic implementation, we must assume that, once introduced, vaccination passports will be applied to international travel. We argue that the global health community must participate in the debate about such schemes with an eye to equity and highlight their global implications. We emphasise two concerns. First, vaccination passports attached to international travel before equitable access to vaccines has been established will exacerbate global inequalities by effectively excluding from travel the millions of citizens of poor countries who are suffering from the health impact and the social and economic fallout of the pandemic, with little prospect of gaining access to vaccines any time soon. Such inequalities are rooted in a long history of colonialism and exploitation; global restrictions based on vaccination status will amplify these historic injustices. Second, such schemes will undermine global solidarity, increasing the risk that the pandemic is seen as a local issue rather than a genuinely global problem. Vaccination passports raise many ethical issues. Arguments for restrictions on people’s freedoms, such as lockdowns or quarantines, lose their traction when individuals have been fully vaccinated and therefore—as increasing evidence …
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 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".