Rethinking planetary health assumptions about ontology through ethnography
Bibliographic record
Abstract
Background Planetary health seeks to promote human and ecological wellbeing, emphasising the interconnectedness of humans as part of nature and the inclusion of Indigenous Knowledges. However, critiques highlight the reliance of Planetary Health on the Western ontological categories of nature and human, and the low engagement with indigenous worldviews. In this study, I examined whether the terms nature and human can effectively convey how Mexican grassroots-level land-defence organisations promote planetary health. Methods 9 months of ethnographic fieldwork (November 2023 to August 2024) was conducted in El Salto and Juanacatlán in Mexico, which are communities severely impacted by industrial pollution, to learn from two grassroot-level organisations, Un Salto de Vida (A Leap of Life) and the Concejo Indígena de Xonacatlán (Xonacatlán Indigenous Council). I observed participants and conducted interviews and storytelling workshops to examine the relationship between local practices and the ontological assumptions of planetary health (ie, a single nature and a universal human) using an abductive data analysis approach. Findings Two ethnographic stories illustrate these challenges. The first contrasts two ways of making pollution real: one based on threshold theories, the other on the disappearance of native animals. The second story examines Indigenous Coca resurgence in Juanacatlán, highlighting the risks of studying using a non-indigenous lens. Interpretation The first story disrupts the assumption of a single nature by revealing two ways to enact pollution. The second story questions the universal human by exposing Indigenous- mestizo power relations in development projects that might harm planetary health. Overall, this study calls for meaningful engagement with grassroots and indigenous ontologies in planetary health scholarship. Funding The Vanier Canada Graduate Scholarship and Pierre Elliot Trudeau Foundation scholarship supported this work as part of the authors' graduate studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".