Necessary conditions for the future consideration of vaccine certificates
Bibliographic record
Abstract
Vaccine certificates were deployed during the COVID-19 pandemic to enable the partial restoration of social and economic activities whilst protecting the public’s health. Despite widespread adoption, their use was (and remains) controversial, especially because of concerns regarding their impact on liberty. Getting clarity on the conditions under which vaccine certificates should and should not be considered is critical as the world prepares for future infectious disease threats, including those that may differ substantially from the COVID-19 pandemic. To this end, we offer a framework which argues that as three key factors increase – the pathogenicity of the infectious disease, the prevalence of the infectious disease, and the protective effects of its associated vaccine – so, too, does the pro tanto justification for considering the use of vaccine certificates, while lower levels diminish the justification. This is because higher ‘scores’ for each of these dimensions will provide stronger justification for trade-offs with liberty that are likely to occur as a result of vaccine certificate use. While not a comprehensive framework for evaluating the use of vaccine certificates, these three conditions comprise a framework that can aid decision-makers in determining whether vaccine certificates are worthy of further consideration in the face of a future threat.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".