Bibliographic record
Abstract
Okay, we’re sold. But now what? (Question asked by a police chief) About 20 years ago, Lawrence Sherman (1998) wrote an article for the National Police Foundation in the USA that set out two important challenges: For police agencies: to participate in the creation and use of high-quality research to guide the development of evidence-based policy, programs and practices. For researchers: to craft scientific work that can be readily understood and used by police services. The rationale? To learn from what the combination of science and police expertise can tell us ‘works’ (and what doesn’t) in relation to various aspects of public policing, from reducing burglaries to deterring gun violence. Since Sherman’s famous challenge, we’ve seen the global growth of evidence-based policing (EBP) into a movement of sorts that has spawned four Societies of Evidence-Based Policing (in the UK, Canada, the USA, and Australia and New Zealand) numbering thousands of members. There are annual conferences, training seminars, books, articles, and various tools and resources to help the budding EBP practitioner. But so far, despite various attempts at laying out some general ideas and principles (see Martin, 2018), what no one has been willing to tackle is the dreaded question posed to us after nearly every presentation or training session we’ve led: ‘How do you actually do this stuff?’ In this book, we tackle this question in a practical, nuts and bolts sort of way, offering ideas and suggestions drawn from both our own research into embedding EBP and from the broader research literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.531 | 0.355 |
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".