Expressing uncertainty in criminology: Applying insights from scientific communication to evidence-based policing
Bibliographic record
Abstract
Scholars and practitioners who develop evidence-based crime policy debate on how best to translate criminological knowledge into better criminal justice practices. These debates highlight the counterpoised problems of over-selling the contribution of scientific evidence; or, alternately, overemphasizing the limitations of science. This challenge attends any attempt to translate research findings into practice; however, and problematically, in criminology this challenge is rarely approached in a theoretically coherent fashion. This article therefore seeks to theorize uncertainty in criminology by examining insights on communicating scientific uncertainty in other fields, and applying these insights specifically to the field of Evidence-Based Policing (EBP). Taking the position that all science is inherently uncertain, we examine the following four aspects of the field: the particular uncertainties of criminology, variance in receptivity to research, the lack of evidence regarding effective communication, and the boundaries of evidence. Building on this analysis, we set out the normative challenge of how researchers should characterize and balance the implications and limits of scientific findings in the decision-making process. Looking ahead, we argue for the need to invest in an empirical project for determining meaningful strategies to express research evidence to decision-makers.
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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 | 0.137 | 0.254 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.012 | 0.118 |
| Scholarly communication | 0.038 | 0.048 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".