Bibliographic record
Abstract
Until the early 1970s, the United States and Canada both had relatively stable imprisonment rates. This paper uses Canada’s continued stability in its rate of incarceration since this period to develop two intertwined explanations for the growth in US imprisonment between 1973 and 2010. First, using data on the relative size of the growth in imprisonment of the individual states, it presents findings that suggest that increased imprisonment was intimately linked to underlying social values. For instance, those states with the largest increases in incarceration were, in terms of the values of their citizens, least “Canadian-like.” In addition, high imprisonment states tended to have values favoring social exclusion. Second, we argue that the United States has consistently demonstrated penal optimism—that is, a strong faith in the ability of the criminal justice system to reduce crime. Prior to the mid-1970s, it was broadly believed that the recourse to prison through a rehabilitation model whereby offenders were treated or “cured” could reduce crime. Starting in the mid-1970s, the focus of optimism changed such that crime was now seen as being able to be controlled through the deterrent and incapacitative effects of high imprisonment. In contrast, from the mid-nineteenth century onwards, Canada has never been optimistic that the criminal justice system—through any mechanism—could have a substantial impact on crime rates. By extension, imprisonment was seen as a necessary evil to be minimized as much as possible.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".