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Record W3035560896 · doi:10.1177/2043820620934209

On the relationships between COVID-19 and extended urbanization

2020· article· en· W3035560896 on OpenAlexaff
Creighton Connolly, S. Harris Ali, Roger Keil

Bibliographic record

VenueDialogues in Human Geography · 2020
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsYork University
Fundersnot available
KeywordsUrbanizationCoronavirus disease 2019 (COVID-19)PandemicOutbreakEconomic geographyInfectious disease (medical specialty)2019-20 coronavirus outbreakCorporate governanceSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)GeographyDevelopment economicsDiseaseEconomic growthEconomicsBiologyVirologyMedicine

Abstract

fetched live from OpenAlex

This commentary focuses on the relationship between extensive forms of urbanization and emerging infectious disease, using empirical examples from the COVID-19 pandemic. Specifically, it examines the role of shifting urban-ecological relationships, infrastructures, and governance dynamics in both giving rise to, and mitigating, the impact of infectious disease outbreaks.

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)0.005
metaresearch head score (Gemma)0.024
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.010
Scholarly communication0.0040.005
Open science0.0010.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.394
GPT teacher head0.402
Teacher spread0.008 · 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

Citations144
Published2020
Admission routes1
Has abstractyes

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