Penal change as penal layering: A case study of proto-prison adoption and capital punishment reduction, 1785–1822
Bibliographic record
Abstract
Recently, scholars have increasingly criticized descriptions of significant penal change as “ruptures”—sudden breaks with past practices, often replacing old technologies with new. This article promotes an alternative understanding of penal change as the layering of new penal technologies over old technologies to describe the complicated coexistence of old and new penal technologies following significant moments of change. This study demonstrates the layering process through a case study of the first major American penal reform: proto-prisons adopted between 1785 and 1822 are often described as the first great rupture in which long-term incarceration replaced capital punishment. Using the relationship between America’s emerging proto-prisons and declining death penalty, this article illustrates the complicated coexistence of penal reforms with older technologies. While proto-prisons emerged out of revulsion with capital punishment, many states adopted proto-prisons independently of their decisions to reduce capital offenses and most states retained relatively robust death penalties. Rather than a replacement or rupture, the emergence of proto-prisons represented an additional layer of punishment that partially displaced older technologies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".