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Record W6943886562 · doi:10.17863/cam.46529

Urbanisation and mortality in Britain c.1800-1850

2019· article· en· W6943886562 on OpenAlexaboutno aff

Bibliographic record

VenueApollo (University of Cambridge) · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsnot available
FundersLeverhulme TrustWellcome Trust
KeywordsUrbanizationQuarter (Canadian coin)Mortality rateStandard of livingEpidemiologyRural areaPopulationDeveloped country

Abstract

fetched live from OpenAlex

In the long-running debate over standards of living during the Industrial Revolution, pessimists have identified deteriorating health conditions in towns as undermining the positive effects of rising real incomes on the ‘biological standard of living’. Here we review long-run historical relationships between urbanisation and epidemiological trends in England, and then address the specific question: did mortality rise especially in rapidly growing industrial and manufacturing towns in the period c.1830 – 1850? Using comparative data for British, European and American cities and selected rural populations we find good evidence for widespread increases in mortality in the second quarter of the nineteenth century. However this phenomenon was not confined to ‘new’ or industrial towns. Instead, mortality rose in the 1830s especially amongst young children (aged one to four years) in a wide range of populations and environments. This pattern of heightened mortality extended between c.1830 and c.1870, and coincided with a well-established rise and decline in scarlet fever virulence and mortality. Our evidence therefore supports claims that mortality worsened for young children in the middle decades of the nineteenth century, but also indicates that this phenomenon was more geographically ubiquitous, less severe, and less chronologically concentrated, than previously argued.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.995

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 studies0.0000.000
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 head0.019
GPT teacher head0.180
Teacher spread0.161 · 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 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

Citations1
Published2019
Admission routes1
Has abstractyes

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