Can legislation prevent debauchery? Mother gin and public health in 18th-century England
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".