Bibliographic record
Abstract
The most memorable figure of the London “Gin Craze,” a furor over cheap spirits from approximately 1720 to 1751, is the woman in the foreground of William Hogarth’s Gin Lane (1751), so deep in stupor that she fails to notice her tumbling child. Hogarth draws on a longer tradition throughout the Gin Craze of using individual drinkers—particularly women—to rhetorically invoke a drug crisis. This essay asks how such figures of individual drinkers come to betoken a larger crisis, and how they establish gin as the cause of that crisis, rather than a symptom of underlying dispossession. I explore how various portrayals of drinkers in Gin Craze discourse each work to exemplify the crisis and to posit gin as its cause. Further, I offer formal as well as historical explanations for why these figures are so often women: how the cultural institutions of eighteenth-century femininity, including maternity and coverture, convinced artists, authors, and readers that women were particularly well-suited to exemplify gin’s compulsions and depredations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".