Do Meta-Analyses of Intervention/Prevention Programs in the Field of Criminology Meet the Tests of Transparency and Reproducibility?
Bibliographic record
Abstract
While assessments of transparent reporting practices in meta-analyses are not uncommon in the field of health sciences interventions, they are limited in the social sciences and to our knowledge are non-existent in criminology. Modified PRISMA 2020 checklists were used to assess transparency and reproducibility of reporting for a sample of 33 meta-analyses of intervention/prevention evaluations published in scholarly journals between 2016 and 2021. Results indicate that the average rate of transparent reporting practices was 63%; adherence varied considerably across studies and subscales, with low rates of adherence for some core checklist items. Overwhelmingly, studies were not reproducible in their entirety; article word count was significantly correlated with reproducibility ( r = 0.4028, p < .03). These findings suggest that substantial changes to reporting practices are necessary to meet traditional meta-analytic claims of transparency and reproducibility. Study limitations include sample size, coding instruments, and coding subjectivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.143 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads 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".