Bibliographic record
Abstract
This article argues that accounts that envisage rupture in penality tend to overplay the coherence of ‘modern’ punishment and underplay the inconsistency of current developments. It suggests that this problem stems in large part from a failure to appreciate the ‘braided’ nature of modern liberal punishment, which is always about both punishment and reform. Part of the ‘secret’ to this is found in David Garland's earlier work in which the ‘welfare sanction’ appears as a compromise between modernist scientific expertise and liberal legalism and individualism. Normative regulation coupled with punishment in this bargain. As a result, even during the heyday of the welfare sanction and at rehabilitation's height, punitive and deterrent penalties remained important. Similarly, there is substantial evidence that increasingly widespread approaches such as restorative justice, therapeutic justice and risk-need models carry a newly revised correctionalism into the present. Rather than conceive recent changes as indicative of a watershed in penal rationality and practice, this article suggests that it is more important to think about the ways in which neo-liberal assaults on the modernist side of this equation have transformed its character.
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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.047 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".