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Record W2036966293 · doi:10.1080/00420980500452458

Global Cities and the Spread of Infectious Disease: The Case of Severe Acute Respiratory Syndrome (SARS) in Toronto, Canada

2006· article· en· W2036966293 on OpenAlexaffabout
S. Harris Ali, Roger Keil

Bibliographic record

VenueUrban Studies · 2006
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsYork University
Fundersnot available
KeywordsOutbreakGlobalizationContext (archaeology)Public healthInfectious disease (medical specialty)DiseaseEconomic growthDevelopment economicsEconomic geographyEnvironmental healthGeographyPolitical scienceVirologyMedicineEconomics

Abstract

fetched live from OpenAlex

The outbreak of severe acute respiratory syndrome (SARS) in Toronto and other cities in 2003 showed a heightened sensitivity of places in the global economy to rapid changes brought on by the acceleration of social and ecological relationships. The spread of the SARS virus may be a predictable consequence of these processes. The paper investigates how processes of globalisation have affected the transmission and response to SARS within the context of the global cities network. Little work has been done on the relationship of global city formation and the spread of infectious disease. Arguing that this relationship may be central to understanding the intricate capillary structures of the globalised network, the paper focuses on how pathogens interact with economic, political and social factors. These relationships exist both in the network and in global cities themselves, thereby posing new issues for public health and epidemiological efforts at disease containment and tracking.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.263

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0070.004
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.064
GPT teacher head0.355
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations156
Published2006
Admission routes2
Has abstractyes

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