German Pharmaceutical Imperialism in Brazil: Cinchona, Biopolitics, and Hybrid Knowledge in the Early 19th Century
Bibliographic record
Abstract
Abstract Between 1817 and 1831, four German scientists – Karl von Martius, Georg Langsdorff, Ludwig Riedel, and Friedrich Sello – undertook expeditions in Brazil with the goal of collecting natural specimens, particularly focusing on Brazilian cinchona plants. Renowned for their medicinal properties, especially in the treatment of fever diseases, cinchona specimens were extensively utilized by local Brazilian communities. The widespread use of cinchona raises important questions regarding how German scientists acquired knowledge of the therapeutic properties of plants, previously unknown within German pharmacology. This paper argues that the German understanding of Brazil's cinchona trees was situated within an imperialist endeavor that not only appropriated indigenous knowledge but also involved conducting experiments on these plants and their effects on local populations. This hybridization of knowledge about cinchona was characterized by an asymmetrical dominance of German pharmacological experimentation, which sought to enhance organic life and establish utopian, “healthy” German societies, in both German territories and Brazil. Consequently, German chemical experiments with Brazilian cinchona specimens intersected with biopolitical practices, aimed at manipulating both plant and human life through therapeutic interventions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".