Bibliographic record
Abstract
Prior to the 1990s, policing across the West was largely conducted through what is known as the ‘standard model’ of policing (Sherman, 2013). This model, also known as the ‘3Rs’, was a ‘one-size-fits-all’ reactive approach that placed heavy emphasis on random patrols, rapid responses to calls for service, and reactive investigations, along with intensive enforcement in the form of police crackdowns and/or saturation policing (Weisburd and Eck, 2004; Sherman, 2013). Despite the apparent popularity of such approaches within policing and political circles, widespread crime rate increases throughout the 1970s and 1980s led some to question the ability of the police to reduce and prevent crime (Bayley, 1994). Growing doubt about the effectiveness of current policing styles, coupled with a growing body of evidence showing that practices under the standard model had little-to-no impact on crime (Skogan and Frydl, 2004), led to a period of significant innovation within policing. While the standard model was not replaced entirely, new policing processes and philosophies were developed and implemented. These innovations included not only evidence-based policing (EBP), but also problemoriented policing (POP), community-oriented policing (COP), CompStat (Computerized Statistics) and intelligence-led policing (ILP). One of the questions we are not infrequently asked is, ‘What is the difference between EBP and POP?’ Another is ‘Does EBP replace COP?’ Given the extent to which there is some overlap among these philosophies, and thus some natural confusion about whether they compete or complement one another, we thought it would be helpful to highlight some of the main characteristics and differences, focusing primarily on how each of these philosophies can be made compatible with an EBP approach. Thus, the purpose of this chapter is to situate EBP next to these other policing innovations. To begin, this chapter will provide an overview of EBP, further expanding on its origins and what Sherman (1998) ultimately intended for it to solve. The focus of the chapter then shifts towards three unique, but complimentary, policing innovations – problem-oriented policing, community policing, and intelligence-led policing – to outline where EBP stands relative to them.
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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 | 0.279 | 0.360 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.024 | 0.011 |
| Science and technology studies | 0.010 | 0.061 |
| Scholarly communication | 0.038 | 0.044 |
| Open science | 0.012 | 0.041 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".