Plague, quarantine, and environmental design in nineteenth century Odesa
Bibliographic record
Abstract
The role of urban planning and architecture in mitigating infectious diseases has lately attracted more scholarly attention. The paper explores an epidemic of plague in nineteenth-century Odesa (then the Russian Empire, now Ukraine) and argues that the city's development was fundamentally linked to activities focused on preventing the disease's reoccurrence and creating a healthy urban environment. It analyzes never discussed visual materials from the collections of the Hermitage Museum, State Museum in Berlin, Canadian Centre for Architecture (CCA), British Museum, and Library of Congress and places them in the context of literary work, mainly travellers’ diaries and memoirs of contemporaries. Although over the last two decades, several publications focused on Odesa's history, literature, culture, and social life came into existence, the urban development and architecture of this metropolis have yet to garner sufficient scholarly attention. The article focuses on primary sources making new attributions of visual materials. It illuminates such essential aspects of urban life as health and hygiene, sanitation, design of open green spaces, and control of air and water supplies. It also helps to understand the architectural solutions for mitigating infectious disease and establishing Odesa as one of the leaders in pandemic-related development at the time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.004 |
| 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".