Bibliographic record
Abstract
In July of 1582, Agnes Waters and four other women of the town of Godalming faced the fearsome spectacle of the queen's justices at the Kingston assizes. The women found themselves caught in the ever-widening gaze of the state, standing trial for a crime only recently defined at law: witchcraft. A jury acquitted the other women, but Waters admitted that she had in fact bewitched ten bullocks and a cow to their deaths. While Waters had the misfortune of being charged under one of the many new statutes, she was saved from punishment by something else that became more readily available in these years: a royal pardon. Punishment and mercy coexisted as strategies of power in the increasingly centralized, increasingly intrusive Tudor state. Both shaped the exercise and experience of authority. In conjunction with the growth in the scope and severity of the law emerged a set of changes that demonstrated the increasing importance of mercy and ensured that people might more easily avail themselves of its benefits. Pardons became more frequent as the years passed, an increase helped in part by changes to the methods with which the Tudors distributed and displayed their clemency. Although late medieval kings had sporadically issued proclamations or statutes of general pardon that offered forgiveness for a host of offenses, these devices appeared with much greater frequency in the sixteenth century and became a standard part of parliamentary business.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".