Bibliographic record
Abstract
Evidence Based PolicingO ne of the questions that both of us have been asked over the past few years is: "What on earth made you get started on this evidence based policing (EBP) stuff?"This question has variously been asked with tones of skepticism, wonder, casual or pointed interest, and/or a certain degree of head shaking.Although our routes to becoming advocates for a movement that promotes the use of quality research in policing are very different-Renée started as a police officer and Laura as an academic researcher-we shared a common belief early on.That belief can succinctly be captured in the phrase: "we suck."For Laura, the pivotal moment came when asked to sit on an expert panel of the Canadian Council of Academies (CCA, 2014).The panel was tasked with the following mandate: predict the future of Canadian policing models based on the available research.There was only one small problem: chronic underfunding of Canadian policing research over the previous 30 years meant that the panel was unable to answer many basic, yet highly important, questions about contemporary policing.Predicting the future based on the Canadian work available would therefore be a nearly impossible task.Indeed, subsequent research into the size and scope of the canon of published Canadian policing studies revealed significant gaps in almost all aspects of policing, including training, recruitment, public oversight, operational issues, and others (Huey, 2016).The Canadian Society of Evidence Based Policing was launched in April 2015 as an effort to reinvigorate domestic policing research and spur home-grown innovation in the field.Renée learned about EBP through a serendipitous meeting at a police conference with Jim Bueermann, the now-retired chief of Redlands Police Department and the current President of the Police Foundation.Learning
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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".