Bibliographic record
Abstract
Throughout this book we have looked at how to get started learning about evidence-based policing (EBP), whether you are an individual looking to educate yourself on the topic or a leader in a large organization attempting to shift cultural values, using agency size as a framework. Although we have used agency size as a framework, some large agencies (Chapter 7) might find solutions in the chapter on small agencies (Chapter 5) useful, and vice versa. That being the case, we thought a chapter dedicated entirely to useful evidence-based resources might be a handy go-to guide, no matter the size of the agency. This chapter is broken down into various EBP Societies, policing organizations, and universities that have dedicated their resources to sharing evidence-based information. We begin with the most obvious resources, the Societies of Evidence-Based Policing, and review some of what each of the Societies offers for their members. Evidence-Based Policing Societies UK Society of Evidence Based Policing (SEBP) The first Society of Evidence Based Policing (SEBP) was formed in the UK, which is why their name and website does not indicate a country. At the time it was established, there were no similar societies. Because other countries soon followed suit and created similar societies, SEBP is referred to in the literature and in conferences as the UK Society of Evidence Based Policing, but on its website (see www.sebp.police.uk), its official name is the Society of Evidence Based Policing. It was founded in 2010 and is open ‘to any member of police staff or researcher who is committed to making a positive impact in the community through using the best available research evidence’, and is ‘made up of police officers, police staff and research professionals who aim to make evidence based methodology part of everyday policing in the UK’ (www.sebp.police.uk). You do not have to be a British citizen to be a member, and membership is free. The goals of SEBP are: • To increase the use of best available research evidence to solve police problems. • To produce new research evidence by police practitioners and researchers. • To communicate research evidence to police practitioners and the public.
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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 | 0.062 | 0.277 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.023 | 0.029 |
| Open science | 0.008 | 0.022 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.259 | 0.190 |
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".