Bibliographic record
Abstract
The study of the royal power of pardon illuminates the English criminal justice system, particularly in the eighteenth century. Pardons granted on condition of transportation acted as a counterbalance to the harshness of the “Bloody Code”, notably after 1689 when a considerable increase in the number of capital statutes threatened a vast rise in executions. The documents generated by the pardon process, especially petitions and judges' reports, suggest the boundaries within which the royal authority was exercised and the relative weight given to the nature of the offence, the character of the accused and the influence of the social and political elite. A study of those who were pardoned and those on the other hand who were hanged reveals that the overriding aim of those who administered the criminal law was to interpret and enforce the law so as to enhance its terror while underlining the king's justice and humanity. The royal power of pardon was an essential element in that administration of the law. During the late-eighteenth and early-nineteenth centuries, the convictions and attitudes which supported the criminal justice system came under scrutiny, and the cruelty and capriciousness of capital punishment was the subject of particular criticism. The reform of the law in the early decades of the nineteenth century sharply curtailed the role that the royal pardon had played in the administration of justice for several centuries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".