Health, environment and colonial legacies: Situating the science of pesticides, bananas and bodies in Ecuador
Bibliographic record
Abstract
Pesticide-related health impacts in Ecuador's banana industry illustrate the need to understand science's social production in the context of major North-South inequities. This paper explores colonialism's ongoing context-specific relationships to science, and what these imply for population health inquiry and praxis. Themes in postcolonial science and technology studies and critical Latin American scholarship guide this exploration, oriented around an ethnographic case study of bananas, pesticides and health in Ecuador. The challenge of explaining these impacts prompts us to explore discursive and contextual dynamics of pesticide toxicology and phytopathology, two disciplines integral to understanding pesticide-health linkages. The evolution of banana phytopathology reflects patterns of banana production and plant science in settings made accessible to scientists by European colonialism and American military interventions. Similarly, American foreign policy in Cold War-era Latin America created conditions for widespread pesticide exposures and accompanying health science research. Neocolonial representations of the global South interacted with these material realities in fostering generation of scientific knowledge. Implications for health praxis include troubling celebratory portrayals of global interconnectedness in the field of global health, motivating critical political economy and radical community-based approaches in their place. Another implication is a challenge to conciliatory corporate engagement approaches in health research, given banana production's symbiosis of scientifically 'productive' military and corporate initiatives. Similarly, the origins and evolution of toxicology should promote humility and precautionary approaches in addressing environmental injustices such as pesticide toxicity, given the role of corporate actors in promoting systematic underestimation of risk to vulnerable populations. Perhaps most unsettlingly, the very structures and processes that drive health inequities in Ecuador's banana industry simultaneously shape production of knowledge about those inequities. Public health scholars should thus move beyond simply carrying out more, or better, studies, and pursue the structural changes needed to redress historical and ongoing injustices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".