Bibliographic record
Abstract
Research Summary In our article, we attend to the implied outlooks (“philosophies” in the sense of operative practical discourses and assumptions) and the competing ethical concerns that animate differing views on privatizing corrections. We consider some normative arguments and empirical observations that have been mobilized for and against privatization since the inception of the modern version of this debate in the late 1980s, and we seek to place these in the context of accounts of penal problems over that contentious period. We argue that a multidimensional approach to understanding the sociology of punishment and in particular how certain forms of punishment persist, survive, and thrive is required when considering the privatization of corrections. In using such an approach, we raise quizzical questions regarding the pairing together of punishment and privatization, and as a result, we seek to sharpen the discussion about future prospects. Policy Implications Greater attention must be paid toward public involvement, knowledge, and understanding about penal policies. With particular regard to the involvement of private‐sector actors and interests, this has implications both at the initial contract negotiation stages of expanding correctional privatization as well as at the rescission of such contracts. The impact of penal arrangements on the dignity and integrity of offenders—especially but not only prison inmates—their loved ones and communities, and wider considerations of public interest are abiding and unresolved concerns, and privatization policies must be evaluated in light of these.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.052 | 0.008 |
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".