Bibliographic record
Abstract
This article examines the discrepancies between theories of risk and penality and emergent strategies of risk/need identification and management. Working back from the strategies themselves, I argue that the current generations of risk/need technologies are a significant departure from the pessimistic theoretical accounts of risk in criminal justice associated with the ‘new penology’ and ‘actuarial justice’. I argue that risk knowledges are fluid and flexible and capable of supporting a range of penal strategies. The evolution and meanings of risk in correctional assessment and classification are examined to show how understandings of risk have shifted from static to dynamic categorizations. I show how the concept of need is fused with risk, how particular conceptions of ‘need’ and ‘risk’ are situated in local penal narratives, how need reconstructs risk and revives correctional treatment as an efficient risk minimization strategy. I argue that strategic alignment of risk with narrowly defined intervenable needs contributes to the production of a transformative risk subject who unlike the ‘ fixed or static risk subject’ is amenable to targeted therapeutic interventions. Newly formed risk/needs categorizations and subsequent management strategies give rise to a new politics of punishment, in which different risk/needs groupings compete for limited resources, discredit collective group claims to resources, redistribute responsibilities for risk/needs management and legitimate both inclusive and exclusionary penal strategies.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".