International Rights Affecting the COVID–19 Vaccine Race
Bibliographic record
Abstract
The impact of the COVID–19 pandemic has been felt world-wide, and despite having several vaccines in the market at this point, there are still issues of accessibility for certain countries. International intellectual property law has been a breeding ground for the exploration of intellectual curiosity and creation as it provides strong protections to creators. These strong protections have allowed for the monopolization of certain goods, such as vaccines, under the concept of patents. While patents are important to incentivize pharmaceutical companies to create life–saving medicines, these protections have also become a barrier for access to medicines, especially in less–developed countries. This Note seeks to address the interplay between international intellectual property rights and the right to health under the inter-national human rights framework. Specifically, it will dis-cuss the two differing rights through the United States and Canada’s efforts to promote creation of COVID–19 vaccine candidates. In order to highlight the financial driver behind patent protections, this note will compare the production and patenting process of the COVID–19 vaccines, a virus that also heavily impacted developed countries, versus the under–funded Ebola virus, which predominantly effected less–developed countries. Finally, this Note will offer recommendations on how countries, and pharmaceutical companies, can take a human rights approach by utilizing patent protection exceptions in order to make COVID–19 vaccines accessible to all countries.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.019 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".