The impact of nonprogrammatic factors on criminal‐justice interventions
Bibliographic record
Abstract
Purpose. drew attention to a distinction between ‘programmatic’ and ‘non‐programmatic’ aspects of criminal justice interventions. While a considerable amount of research has accumulated on the former, the latter by comparison remains under‐researched. Nevertheless some advances have been made and the present article identifies the key components of this. Methods. Following analysis of the concepts forwarded by Palmer, a methodical comparison is made between his findings on programmatic elements and those of two other major groups of meta‐analytic findings from this area. This provides further opportunity for testing of the Risk‐Needs‐Responsivity (RNR) model and an evaluation is offered of its current status in synthesizing relevant knowledge. A parallel set of comparisons is then drawn with respect to non‐programmatic factors and the paper considers the level of agreement between separate reviews of that knowledge base. This directs attention to a number of instances of intervention ‘failure’ which can be explained by insufficient attention to non‐programmatic issues. Results. There is a generally high level of agreement between the three sets of data surveyed. There is not a complete consensus however, caused not by disagreement between data sets but by gaps in the types and range of evidence assembled. There are larger gaps remaining on non‐programmatic factors and the nature and extent of those is described. There is also discussion of some objections and proposed alternatives to RNR, and to some conceptual confusions arising from them. Conclusions. The present state of knowledge on criminal justice interventions is a ‘work in progress’ but nevertheless can provide firm guidance on the design of such interventions, highlighting areas in which much further work is needed.
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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 | 0.085 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".