A Global Index to Quantify Discrimination Resulting from COVID-19 Pandemic Response Policies
Bibliographic record
Abstract
Immediately following the emergency use authorizations of COVID-19 vaccines, governments around the world made these products available to their populations and later started implementing differential rules for vaccinated and unvaccinated citizens regarding mobility and access to venues and services. The Oxford COVID-19 Government Response Tracker (OxCGRT) is a time series database that reflects the extent of public health measures in each country. On the basis of the OxCGRT Containment and Health Index, we calculated a corresponding discrimination index by subtracting the daily index values for vaccinated and unvaccinated individuals. The resulting metric provides a cursory quantification of the discrimination experienced by unvaccinated individuals throughout 2021 and 2022. Patterns in the index data show a high degree of discrimination with great numeric and temporal differences between jurisdictions. Around 90% of countries in Europe and North and South America discriminated against their unvaccinated citizens at some point during the pandemic. The least amount of discrimination was found for countries in Central America and Africa. In order to move towards sustainable post-pandemic recovery and prevent discriminatory public health policies in the future, we recommend that human rights protections be expanded and the prohibition of discrimination be extended beyond a limited list of grounds.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".