Bibliographic record
Abstract
ABSTRACT ‘Mass incarceration’, as conventionally understood, refers to an imprisoned population that is both excessive in size and racially skewed in its demographics. However, in contrast to racial skew, the appropriate size of a prison system has largely escaped analysis. This article contributes to analysis of the scale of a prison system in two ways. First, I show why non‐controversial principles linking crime to punishment, such as guilt and proportionality, are insufficient. Because incarceration rates are driven more by social policy than by crime, an adequate analysis of scale presupposes an account of what we hope to get out of punishing people in the first place. Second, drawing on a generic crime‐prevention account of incarceration, I sketch three increasingly resolving, but also increasingly contentious, conceptions of excess: the Pareto, social welfare, and utilitarian conceptions. Along the way, I briefly consider the trade‐off between how committal a theory of incarceration is and its ability to explain what is wrong with mass incarceration, as well as the concern that the social welfare and, especially, utilitarian concepts are excessively paternalistic. The ultimate aim of the article is to contribute to our understanding of mass incarceration as a distinctive normative concept.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".