Methodological Quality and Validity Issues in the Crime Prevention Literature
Bibliographic record
Abstract
This paper presents an assessment of the existing literature on validity and methodology relevant to crime prevention studies. Reports eligible for inclusion in the review focused on assessing the methodological quality of crime prevention evaluations. A narrative synthesis approach was used to review the included reports to examine how validity considerations are assessed and addressed in criminological impact evaluations. The reports reviewed included substantive discussions of the five types of validity, as well as discussions of interrelated issues of evaluation design, methodological quality scales, and evidence-based registries. We recommend that all crime prevention evaluations address the methodological issues discussed in this article. In addition, policymakers should consume research with a critical eye toward potential validity issues. Where valid evaluations show interventions to be promising, practitioners should make efforts to ensure fidelity in program implementation. Registries should support policymakers and practitioners in identifying and implementing evidence-based policy and programming through providing guidance on choosing interventions aligned to their priorities and settings, understanding the strengths and weaknesses of various strategies and programs and the conditions under which they are successful, and implementing programs and replicating evaluations with fidelity.
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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 | 0.784 | 0.912 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.044 | 0.037 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.026 | 0.017 |
| Open science | 0.009 | 0.018 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".