Bibliographic record
Abstract
This paper starts and ends with the English pub.And the concern here is for how it was policed, from the seventeenth century up to the First World War.Interconnected are concepts of drunkenness, idleness, disorder, class and social control; all of them key variables when analyzing the politics behind England's many drinking houses, and how they developed.Our lens is one of 'policing': a working philosophy developed by European police theorists to govern entire populations towards productivity.Policing is about social control, about adapting unwanted behaviours and peoples towards favourable ends (generally bourgeois and industrial).Public houses for the poor and working class (alehouses, ginshops, beerhouses etc.) were, consistently, accused by England's ruling interests (and others) of breeding drunkenness, idleness, and social degeneracy.Slated, regularly, for regulation and reform; pubs became ideological sites of class conflict where Drink's many interests battled for moral influence and political spaces.Early policing methods were the initiatives of individual Kings and rogue rulers, but the nineteenth century witnessed comprehensive (and sometimes united) policing programmes aimed at reforming and reducing drinking houses en masse.Action and reaction; these battles between groups, and within groups, would shape the pub.
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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 | 0.011 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".