Assembling Risk and the Restructuring of Penal Control
Bibliographic record
Abstract
In this paper, we draw attention to new assemblages with risk in order to highlight the multiple forms of knowledge and logics at work in new risk assessment practices. We seek to complicate the theoretical explanations of risk by highlighting how risk logics merge and shift in tandem with various rationalities. For example, when risk is merged with need, needs are reconfigured as criminogenic needs but, in this process, risk becomes a fluid concept that can be treated, altered and transformed. When risk is merged with more welfare and disciplinary-based logics, such as rehabilitation and clinical assessments, new forms of risk management are produced, such as targeted treatment. Through these processes, risk’s association with actuarial calculations is weakened by other judgments and appraisals. As well, risk takes on more productive ameliorative possibilities, associated with risk minimization. These new assemblages enable new forms of risk-based governance as evident in contemporary correctional case management planning and the accreditation of programmes. This analysis is developed through an examination of the Level of Service Inventory (LSI)—an internationally used risk assessment instrument.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.007 | 0.060 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".