Bibliographic record
Abstract
In the fall of 1892, ten years into the fifth international cholera epidemic that lasted from 1881 to 1896, fear of cholera in North America, particularly in Toronto, was full blown. Cholera had been raging in the Middle East, India, and Europe, and in Russia alone there were an estimated 300,000 deaths, but the disease had yet to cross the Atlantic Ocean. Maritime traffic of immigrants from Europe was continuous, and each migrant ship potentially carried the disease. Doctors, government officials, and politicians were not asking ‘would cholera come?’ but rather, when. In the city of Toronto, no one actually got sick or died from cholera in 1892. However, the crisis and fears of imminent cholera were real. This article documents how future threats became immediate and dire concerns. My task here becomes how to write a history of an event that was shaped by urgency, immediacy, and speculation on the future. My argument will show how the geography of an epidemic is not limited to the presence of disease. How do you theorize a crisis in the absence of an actual disease outbreak? How do you theorize an event that didn’t happen? This article will answer these questions and contribute to recent literature in geography that engages with life, security, and the future. Predictions about both the present and the future were speculative statements, and these statements had effects on cities and nations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".