Sara E. Black, <i>Drugging France: Mind-Altering Medicine in the Long Nineteenth Century</i>
Bibliographic record
Abstract
History of drugs in the nineteenth century traditionally reveals the extent of the circulation and uses of psychotropic substances in societies at a time there were neither prohibition nor ‘war on drugs’, nor even clear boundaries between medicines and illicit drugs. In that perspective, examining France between 1840 and 1920, Sara Black questions the ambiguities of the ‘pharmakon’, being at the same time poison and remedy. Being attentive to the variety of actors dealing with drugs (physicians, pharmacists, writers, criminologists…), this remarkable book is based on a very deep work on archives (from national records, Paris Police Prefecture, medical libraries of ‘historic’ hospitals, etc. alongside with an impressive number of medical periodicals and published sources). The author exhumes a large number of fascinating case studies, not only centred on Paris, making the reading very gripping. However, we can regret a certain lack of distance from the materials. Wanting to share absolutely with the reader the pleasure of dealing with very rich qualitative data, the author sometimes misses to identify broad lines of synthesis and argumentation. Discussing the point of view of the producer of the source could have been commendable also. Besides, the chapters are roughly hewn, too voluminous, giving also the impression of a series of insights on the questions of drugs, sometimes too rapidly justified. A more conceptualised approach could have been applied: philosophical reflection about the ‘biopower’, links between anxieties about drug use and modern nation-state building…
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.065 | 0.028 |
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".