Grappling with Morphine: A Local History of Painkiller Use in Kerala, India
Bibliographic record
Abstract
In this article I argue that the scarcity of painkillers in the Global South is driven by a central asymmetry in which the health of developed countries is valued over that of the much poorer countries that comprise the rest of the world. To elucidate this point, I argue that by examining the history of various legal institutions and specific events, like the opioid crisis, that have shaped the global production of opium and production in India, one will be able to see the genealogy of the imbalance and inequality that has always affected care. I turn to the state of Kerala to explore instances in which these legal inflections live within the contemporary guidelines for palliative care, and thus constantly affect the supply and delivery of care. This also ties in with the much longer history of opium control for the growth of the global pharmaceutical industry, within which India has been uniquely placed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".