Bibliographic record
Abstract
At first glance, medicine and poison might seem to be opposites. But in China’s formative era of pharmacy (200–800 CE), poisons were strategically deployed as healing agents to cure everything from chills to pains to epidemics. Healing with Poisons explores the ways physicians, religious devotees, court officials, and laypeople used powerful substances to both treat intractable illnesses and enhance life. It illustrates how the Chinese concept of du—a word carrying a core meaning of “potency”—led practitioners to devise a variety of techniques to transform dangerous poisons into efficacious medicines. Recounting scandals and controversies involving poisons from the Era of Division to the early Tang period, Yan Liu considers how the concept of du was central to the ways people of medieval China perceived both their bodies and the body politic. Liu also examines a wide range of du-possessing minerals, plants, and animal products in classical Chinese pharmacy, including the highly poisonous herb aconite and the popular arsenic drug Five-Stone Powder. By recovering alternative modes of understanding wellness and the body’s interaction with potent medicines, this study cautions against arbitrary classifications and exemplifies the importance of paying attention to the technical, political, and cultural conditions in which substances become truly meaningful. Healing with Poisons is freely available in an open access edition thanks to TOME (Toward an Open Monograph Ecosystem) and the generous support of the University at Buffalo Libraries. Additional support was provided by the Chiang Ching-kuo Foundation for International Scholarly Exchange, the Julian Park Publication Fund of the College of Arts and Sciences at the University at Buffalo, and the Traditional Chinese Culture and Society Book Fund.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".