And you know, we’re on each other’s team: Lessons from an in-depth analysis of researcher–practitioner partnerships in criminal justice research
Bibliographic record
Abstract
Researcher–practitioner partnerships (RPPs) bring together multiple perspectives to create more holistic, contextually grounded, and arguably better criminal justice solutions. However, a better understanding of how these partnerships operate and what contributes to success is needed, particularly for new and emerging scholars who may be inexperienced in creating and sustaining RPPs. Previous research has explored the process of creating RPPs, but much of this research is limited by singular partnerships or anecdotal experiences. Further, this research does not account for the myriad of RPP types within the criminal justice field. Our study involves an analysis of 20 years of National Institute of Justice-funded RPPs in the United States. Our findings identify clear themes from this extensive review of the literature and provide empirically based lessons learned for RPPs. As such, this study is designed to support new and established researchers with guidance on how to better create and maintain RPPs.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".