Justice in Global Pandemic Influenza Preparedness: An Analysis Based on the Values of Contribution, Ownership and Reciprocity
Bibliographic record
Abstract
In December 2006, Indonesia decided to stop sending influenza virus specimens to the World Health Organization’s Global Influenza Surveillance Network (GISN). Indonesia justified its actions by claiming that they were in protest of the injustice of GISN. Its actions stimulated negotiations to improve the workings of GISN by developing and implementing a more just framework for ‘sharing influenza viruses and other benefits’. These negotiations eventually led to the adoption of a new framework for virus and benefit sharing in May 2011, at the World Health Assembly meeting. In this article, we critically evaluate Indonesia’s claims about the unjustness of GISN. We show that arguments based on the values of ownership, contribution and reciprocity work together to support Indonesia’s claim that it was owed an equal share in the benefits of GISN and, in turn, that GISN was unjust because of its failure to ensure this. We also use these values to evaluate the newly agreed upon framework for virus and benefit sharing. We suggest the new framework fails to give proper consideration to the values of ownership, contribution and reciprocity and, as a result, that it is fundamentally unjust.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".