Clare Griffin, <i>Mixing Medicines: The Global Drug Trade and Early Modern Russia</i>
Bibliographic record
Abstract
For over two centuries, historians have examined the records of the Muscovite Apothecary Chancery of the seventeenth century to uncover the history of medicine in premodern Russia. Most scholarship has focused on the foreign practitioners employed by the Chancery. By shifting attention to the medicines themselves, Clare Griffin makes a novel contribution to long-standing discussions. Griffin takes inspiration from two current areas of scholarly investigation: the history of material things in a global context, and post-colonial critiques of the dissemination of medical knowledge. Through these lenses, she educes important information from sources previously neglected, most notably the prescriptions doctors wrote and the records of medicines purchased and stocked in the Muscovite state-run pharmacy. These documents consist of little but lists of materia medica, lacking even notations of the illnesses the medicines were expected to treat. Yet Griffin ably analyses them to provide new insights on Muscovy’s place in the world and on the functioning of the Apothecary Chancery itself.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.077 | 0.034 |
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".