Bibliographic record
Abstract
As we’ve observed with numerous policing initiatives over the years, programs can quickly become derailed as a result of a lack of forethought to the issue of long-term sustainability (Willis et al, 2007; Bradley and Nixon, 2009; Kalyal et al, 2018). Of all the topics we cover in this book, this is likely the one that is easiest to describe in theory but that will generate the most difficulty in practice. The reason for this is simple: the adoption of evidence-based policing (EBP) requires not only a degree of investment in individuals as change agents, but also, for some agencies, what might appear to be a radical rethinking of how police services engage in decision-making. While we recognize that the exigencies of policing can sometimes be best met by the traditional command and control structure, an evidencebased organization is one that embodies a learning culture – that is, an institutional culture that places emphasis on seeing operational and administrative decisions as opportunities for implementing innovation and learning lessons from both success and failure. It is also one that requires a more decentralized approach to viewing expertise and soliciting input and feedback into decision-making. In this chapter, we draw on the relevant management and other literatures, as well as on our own and others’ experiences, to make some specific and some broader recommendations for how to generate a sustainable EBP approach within small organizations. Practical solutions Investing in internal resources A study was recently published showing that police officers experience increased job satisfaction when engaged in problemsolving activities within their communities (Sytsma and Piza, 2018). This should hardly be surprising. When organizations hire intelligent, thoughtful, analytical people with diverse knowledge and skills, those same people want to do work that is both intellectually and emotionally satisfying. Or, as one of us recently posted, ‘let smart people do smart work.’ The reality, however, can be very different. Another study, this one on crime analysts, revealed that very few of those surveyed were involved in program evaluation or other forms of experimentation (Piza and Feng, 2017). As the authors suggest – and we agree – this is a tremendous waste of internal resources (Piza and Feng, 2017), and can lead to some analysts feeling their work is little more than ‘wallpaper’ (Innes et al, 2005: 52).
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.400 | 0.001 |
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; both teacher heads 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".