How extractive industries affect health: Political economy underpinnings and pathways
Bibliographic record
Abstract
A systematic and theoretically informed analysis of how extractive industries affect health outcomes and health inequities is overdue. Informed by the work of Saskia Sassen on "logics of extraction," we adopt an expansive definition of extractive industries to include (for example) large-scale foreign acquisitions of agricultural land for export production. To ground our analysis in concrete place-based evidence, we begin with a brief review of four case examples of major extractive activities. We then analyze the political economy of extractivism, focusing on the societal structures, processes, and relationships of power that drive and enable extraction. Next, we examine how this global order shapes and interacts with politics, institutions, and policies at the state/national level contextualizing extractive activity. Having provided necessary context, we posit a set of pathways that link the global political economy and national politics and institutional practices surrounding extraction to health outcomes and their distribution. These pathways involve both direct health effects, such as toxic work and environmental exposures and assassination of activists, and indirect effects, including sustained impoverishment, water insecurity, and stress-related ailments. We conclude with some reflections on the need for future research on the health and health equity implications of the global extractive order.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".