Cancer and Global Environmental Politics: Proposing a New Research Agenda
Bibliographic record
Abstract
More than six million people die of cancer every year. Over the next two decades, the World Health Organization predicts global cancer rates will rise to 10 million deaths annually. What is the impact of the global political and economic processes of environmental change on cancer rates? Why, given the strong intuitive reasons to worry about the carcinogenic effects of global environmental change, is there so little research on this topic? What is the political role of science, corporations, nongovernmental organizations and international institutions on cancer research and cancer rates? What is the impact of global patterns of trade, financing, production and consumption on research and rates? This article charts the current social science literature on cancer and global environmental change with the hope of encouraging scholars of global environmental politics to pursue a new research agenda around questions like these.
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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 | 0.022 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.018 | 0.036 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.018 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 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".