Pardon and Parole in Prohibition-Era New York: Discretionary Justice in the Administrative State
Bibliographic record
Abstract
Historians of early-modern England and British colonies have productively applied Douglas Hay’s germinal study of mercy. In contrast, historians of the United States have overlooked the utility of the conceptual tools Hay provided to prize open the mitigation of punishment across time and place. In the decade that followed the First World War, disputes over the proper role of mercy and administrative discretion were as heated as they were in Hanoverian England. In Jazz Age New York, fears of gangsterism and concern over the apparent laxity of parole regulations put the proponents of Progressive penology on the defensive. This article asks what drove opinion against discretionary justice in the form of the pardon and parole, and traces the conditions that gave rise to judgments that discretionary justice was too frequent and injudicious. A new vision of order, fixated on penal certainty, came into sharp focus over the 1920s, when mandatory sentencing statutes were introduced. Yet gubernatorial clemency survived that crisis, and in 1930 parole was professionalized and placed under stricter management. This article confirms that modernity proved no match for discretionary justice. In its personal and administrative forms, discretion penetrates penal justice, despite the earnest drive to certainty and the persistent demands to terrorize criminals.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".