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Record W4367307681 · doi:10.1080/02665433.2023.2204494

Plague, quarantine, and environmental design in nineteenth century Odesa

2023· article· en· W4367307681 on OpenAlexaboutno aff
Maya Gervits

Bibliographic record

VenuePlanning Perspectives · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsQuarantinePlague (disease)Environmental scienceHistoryAncient historyBiologyEcology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.227
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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