Tropical Topographies: Mapping the Malarial in The Calcutta Chromosome
Bibliographic record
Abstract
This paper reads colonial archives of malaria in conjunction with Amitav Ghosh’s futuristic medical thriller The Calcutta Chromosome (1995) and contends that the novel, loosely based on Sir Roland Ross, ruptures narratives of colonial expertise. The colonial expertise on malaria is embodied by Ross, an officer in the Indian Medical Service; this is in contrast with the model of expertise proposed by the novel. While Ross’s expertise is predicated on the domination of nature and controlling diseased tropical landscapes, the novel resists imperial strategies of mapping and disease control. This paper argues that The Calcutta Chromosome presents an alternative attempt to map the malarial, rewriting history by displacing actors such as Ross and instead placing two colonial subjects, Murugan and Mangala, at the centre of new mapping practices. The novel further questions the notion of ‘colonial improvement’ which malaria facilitated in imperial regimes. Deviating from the colonial history of improving the native body and landscape as a cure for malaria, the novel foregrounds subjugated subjects working at the peripheries of laboratories and scientific practices and thus subverts the notion of the ‘improved subject’ by proposing the idea of the mutational, transformational ‘Calcutta chromosome.’
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".