A Realist Evaluation Approach to Unpacking the Impacts of the Sentencing Guidelines
Bibliographic record
Abstract
Evaluations of complex interventions such as sentencing guidelines provide an opportunity to understand the mechanisms by which policies and programs can impact intermediate and long-term outcomes. There is limited previous discussion of the underlying frameworks by which sentencing guidelines can impact outcomes such as crime rates. Guided by a realist evaluation framework, this article examines the impact of linkages of sentencing policy to resource capacity—a cost-control paradigm under which a few states created guidelines to control rising prison populations and expenditures. Additionally, we argue that the moderating influence of this linkage will depend on the severity of the crime. A key conclusion is that in addition to social science theory, evaluation theory is needed to understand how programs work; there is a greater need for identifying conditions under which policies work or do not work. We find the realist approach as a promising approach to build such knowledge.
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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 | 0.125 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".