Bibliographic record
Abstract
Beginning as a symptomatic reading of Arthur Conan Doyle’s use of a fictional African root poison, the Radix pedis diaboli, in “The Adventure of the Devil’s Foot” (1907), and Indian poisoned darts in The Sign of the Four (1890), this article makes some general comments on the history of colonial tropical toxicology, focusing on the Indian aconite ( Aconitum ferox) and its roots ( Radix aconiti indica). Arguably, Doyle had aconite in his mind while creating the fictional African root poison. Victorian toxicologists, who were deeply interested in Indian poisons, created stereotypes of India as congeries of melancholy and culturally backward, industrially primitive, and morally corrupt societies. Doyle’s fictional poisons were influenced by a normative cultural bias that saw tropical pharmakons like aconite with an Orientalizing gaze. By shifting the geographical focus from Doyle’s “Ubangi country” to nineteenth-century India, I draw attention to a larger spectrum of tropical toxicology. The colonial zeal to taxonomize the properties and utility of tropical pharmakons obsessively revolved around their toxic uses as criminal weapons or accidental killers, while marginalizing the medicinal uses that the plant had been historically put to by ancient Indian physicians.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".