Bibliographic record
Abstract
In response to what federal and provincial policymakers deemed a crisis surrounding the sustainability of current funding levels for public policing, Canadian governments turned to researchers in pursuit of evidence-based solutions. Several commissioned studies subsequently documented an inescapable conclusion: successive waves of government de-funding of criminological research had significantly gutted domestic capacity to produce the necessary research. In 2015, one of the authors launched the Canadian Society of Evidence-Based Policing (Can-SEBP) with one goal: to grow the Canadian policing research field by creating tools and programs aimed at empowering policing practitioners to generate, consume, commission and/or participate in research on “what works.” In this chapter, we explore the different strategies Can-SEBP employs to foster a culture of learning within Canadian policing, one in which police begin the process of taking ownership in the field of police science and academic researchers play a supporting role by helping to encourage that growth.
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 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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.006 |
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".