What the Sentencing Commission Ought to Be Doing Reducing Mass Incarceration
Bibliographic record
Abstract
Beginning in the 1970s, the United States embarked on a shift in its penal policies, tripling the percentage of convicted felons sentenced to confinement and doubling the length of their sentences. This shift included a dramatic increase in the prosecution and incarceration of drug offenders. As a result of its move toward long prison sentences, the United States now incarcerates so many people that it has become an outlier; this is not just among developed democracies, but among all nations, including highly punitive states such as Russia and South Africa, and also in comparison to the United States' own long-standing practices. The present rate of incarceration in the United States is currently "almost five times higher than the historical norm prevailing throughout most of the twentieth century." In sum, the United States has a serious over-punishment problem. Our country's imprisonment rate has acquired the name, "mass incarceration," meant to provoke shame about the fact that the world's wealthiest democracy imprisons so many people, even at a time when crime rates have diminished and crime is "not one of the nation's pressing social problems." Most criminal justice scholars agree that our current prison population is too large. They also agree that the impact of imprisonment on the crime rate is modest and that the speed at which people are released from prison bears little relation to the likelihood that they will remain crime free. Many prisoners can serve shorter sentences without triggering an increase in crime. As a result, we can reduce sentence lengths substantially without adversely affecting public safety.
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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 | 0.017 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".