Pharmaceutical nationalism as an instrument to ensure the access to medicines
Bibliographic record
Abstract
Keywords: pharmaceutical nationalism, access to drugs, compulsory licensing, governmentuse, exclusion from intellectual property rights The article concerns the emergence of the phenomena of«pharmaceutical nationalism» in the year of the COVID-19 pandemic. Pharmaceuticalnationalism is manifested in the qualitative and quantitative aspects. In the contextof a qualitative manifestation of pharmaceutical nationalism, we presume the politicalwill on establishing of a new state protectionist policy to local manufacturers ofmedicines, the establishing of preferences, exemptions of the patent monopoly basingon international legal instruments (TRIPS-flex). The quantitative aspect of pharmaceuticalnationalism is the primacy of satisfaction of the needs of the domestic marketof medicines in quantities that could ensure the biological security of individually foreach state, independently of the interests of others. The article also raises the issue ofthe need and means of forming pharmaceutical nationalism in Ukraine.The world community is calling for the demonopolization of research results on theprevention and treatment of COVID-19. WHO invites developers and companies towork together to ensure the disclosure of treatments and methods if they prove effective.International Federation of Library Associations and Institutions also presentedan open letter to WIPO urging WIPO to use all available flexible intellectual propertymechanisms to maximize global access to information (research data) on the treatmentof COVID-19. Canada, Israel and the EU are working to prevent the monopolizationof COVID-19 prevention and treatment.Ukraine should actively work to develop legislation in the field of compulsory licensing,as provided for in Art. 31 TRIPS Agreement. From a political point of view, itis the historical chance of Ukraine to become «he second India» or «the first Ukraine»in Europe in the production of generic medicines and biosimilars.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".