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Record W1591319543 · doi:10.2105/ajph.91.3.375

Can legislation prevent debauchery? Mother gin and public health in 18th-century England

2001· article· en· W1591319543 on OpenAlexafffund
John Harley Warner, Minghao Her, Gerhard Gmel, Jürgen Rehm

Bibliographic record

VenueAmerican Journal of Public Health · 2001
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsUniversity of Toronto
FundersNational Institutes of HealthUniversity of British ColumbiaNational Institute on Alcohol Abuse and AlcoholismUniversity of Toronto
KeywordsEnforcementLegislationConsumption (sociology)Government (linguistics)LegislaturePublic healthLaw enforcementPsychological interventionState (computer science)Environmental healthPublic economicsDemographic economicsMedicineBusinessPolitical scienceEconomicsLawSociologySocial science

Abstract

fetched live from OpenAlex

The "gin epidemic" of 1720 to 1751 in England was the first time that government intervened in a systematic fashion to regulate and control sales of alcohol. The epidemic therefore provides an opportunity to gauge the effects of multiple legislative interventions over time. Toward that end, we employed time series analysis in conjunction with qualitative methodologies to test the interplay of multiple independent variables, including real wages and taxes, on the consumption of distilled spirits from 1700 through 1771. The results showed that each of the 3 major gin acts was successful in the short term only, consistent with the state's limited resources for enforcement at the local level, and that in each instance consumption actually increased shortly thereafter. This was true even of the Gin Act of 1751, which, contrary to the assumptions of contemporaries and many historians, succeeded by accident rather than by design. The results also suggest that the epidemic followed the inverse U-shaped trajectory of more recent drug scares and that consumption declined only after the more deleterious effects of distilled spirits had been experienced by large numbers of people.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.058
GPT teacher head0.266
Teacher spread0.208 · 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 designNot applicable
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

Citations26
Published2001
Admission routes2
Has abstractyes

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