A conceptual kaleidoscope: contemplating ‘dynamic structural risk’ and an uncoupling of risk from need
Bibliographic record
Abstract
Recent calls for ‘evidence-based’ approaches have firmly positioned risk assessment as a promising path towards more efficient, unbiased, and empirically based offender management, in custody and in the community. Simultaneously, sociological and critical legal scholars have questioned the focus on individual needs at the expense of wider structural factors’. I will demonstrate the need to reconceptualise risk/need logics and the use of ‘evidence’. I will argue that various criminal justice processes are themselves dynamic criminogenic risks that produce systemic conditions for recidivism and which, if modified, could make a measurable difference in recidivism and other correctional efficiencies. Finally, I will argue that the logic of dynamic risk is transferable to an analysis of socio-structural factors, and that this characterisation can alter the framing of penal subjects, governmental responsibilities, and potentially interrupt the systemically produced criminogenic pathways that perpetuate criminal involvement and marginalisation.
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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.105 |
| Scholarly communication | 0.015 | 0.034 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".