The Murder Act: Hanging in Chains, 1660–1834
Bibliographic record
Abstract
The Murder Act of 1752 required that criminals who were not dissected to be hung in chains on a gibbet. Yet just as many non-killers were hung in chains during the years 1752–1801 as in 1700–52. And. by the time use of the gibbet was confined strictly to murder, its use against any crime whatsoever had fallen into disfavour. That process was well under way in London no later than 1700 and was apparent in many other places soon after 1750. Urbane people frequently demanded that gibbets be removed to places more remote from respectable residences, and further back from roadsides to avoid offending travellers’ sensibilities. By the nineteenth century, the erection of a gibbet seemed more often an occasion for carnival than a plausible deterrent to crime. Until its abolition in 1834, however, England’s traditional elites clung to the option of the gibbet almost as determinedly as they did to execution for crimes against property.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".