Bibliographic record
Abstract
The entire premise of evidence-based policing (EBP), with its emphasis on ‘what works’, should, in theory, be of immense appeal to police services. After all, we have a sizeable body of research that shows how programs and strategies based on sound research can contribute significantly to police effectiveness and efficiency (Lum, 2009). For example, proactive strategies such as CompStat (Computerized Statistics), problem-oriented policing (POP), intelligence-led policing (ILP) (Ratcliffe, 2008), and hotspots policing (Sherman and Weisburd, 1995) have been found to be more effective compared to random and reactive models. Research by Braga and Weisburd (2010) shows that an evidence-based approach was effective in reducing crime when adopted by police services in the USA by helping officers to focus on areas with a greater incidence of crime. And yet, despite such research, uptake of EBP by police organizations has generally been rather slow (Lum et al, 2012). Why is that? In this chapter, we take an organizational perspective, looking first at factors that affect police receptiveness to EBP, and then later identifying factors that affect resistance. Our goal: to help both police practitioners and their services move beyond various institutional obstacles and stumbling blocks. Factors affecting receptivity to evidence-based policing Practices Evidence-based practices are expected to help police organizations enhance their knowledge base, effectiveness, and economic efficiency (Bierly et al, 2009). One would assume that given the noted benefits of EBP, there would be abundant research identifying factors that help foster receptivity and reduce resistance to these practices. Surprisingly, such studies are few and far between, and a majority of them do not provide a clear idea about the impact of organizational context on embedding EBP in police organizations. So what is organizational context, and why should we care? Allow us to explain. Organizational context refers to the factors that make up the external and internal organizational environment. These factors, individually and collectively, can have the capacity to impede or facilitate organizational change. External environments include political, social, economic, or technological factors (Spector, 2011), while the internal organizational environment comprises of institutional resources, organizational structure, culture, and prior experience of change (Reichers et al, 1997).
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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.153 | 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 teacher head, 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".