Mainstreaming “One Health” in the Inter-American Human Right System’s pandemic actions
Bibliographic record
Abstract
The Inter-American Human Rights System only started to actively integrate the health-environment nexus in response to the devastating impacts of the COVID-19 pandemic on human rights. Two broad dimensions of this nexus can be identified. On the one hand, environment degradation increases the risk of future pandemics by increasing the probabilities of animal-human disease transmission. On the other, environmental degradation can also magnify the health impacts of pandemics once these are declared, especially for vulnerable groups and in the context of climate change. In this context, this article argues that the Inter-American System should mainstream the One Health approach in its pandemic prevention, preparedness, and response actions. This approach, recently adopted by the World Health Organization, would allow the different organs of the Inter-American System and its many participants to envision the health of people, animals, and the environment as fully integrated. In order to provide member states with clear, detailed, and comprehensive guidance, a systematic examination of the legal implications of the One Health approach for the implementation of the Inter-American human rights instruments is urgently needed. This article identifies relevant normative foundations for this task in the Inter-American Court of Human Rights’ interpretation of the rights to a healthy environment, life, and health. It also proposes concrete avenues moving forward. These include a resolution by the Inter-American Commission on Human Rights adopting the One Health approach to pandemic prevention, preparedness, and response; online public consultations and Hearings of a General Nature to gather civil society perspectives; a request that all member states provide information on the domestic measures adopted; and a Thematic Report by the Special Rapporteurship on Economic, Social, Cultural and Environmental Rights dedicated to the legal implications of the One Health approach to pandemics.
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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 | 0.076 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.071 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.026 | 0.042 |
| 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".