An intersectional human rights approach to prioritising access to COVID-19 vaccines
Bibliographic record
Abstract
We finally have a vaccine for the COVID-19 crisis. However, due to the limited numbers of the vaccine, states will have to consider how to prioritise groups who receive the vaccine. In this paper, we argue that the practical implementation of human rights law requires broader consideration of intersectional needs in society and the disproportionate impact that COVID-19 is having on population groups with pre-existing social and medical vulnerabilities. The existing frameworks/mechanisms and proposals for COVID-19 vaccine allocation have shortcomings from a human rights perspective that could be remedied by adopting an intersectional allocative approach. This necessitates that states allocate the first COVID-19 vaccines according to (1) infection risk and severity of pre-existing diseases; (2) social vulnerabilities; and (3) potential financial and social effects of ill health. In line with WHO's guidelines on universal health coverage, a COVID-19 vaccine allocation strategy that it is more consistent with international human rights law should ensure that vaccines are free at the point of service, give priority to the worst off and be allocated in a transparent, participatory and accountable prioritisation process.
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".