Do We Need to Know Each Other? Bridging the Gap Between the University and the Professional Field
Bibliographic record
Abstract
The university and the policing professional field are commonly viewed as separate worlds that have trouble working together, each with their own objectives, values, methods, and processes. It is argued that they need to know each other better and to collaborate for the best of education, professional practice, and research. To achieve this, crime, security, and policing, rather than technology, have to be recognized as a common object of interest. In this perspective, the article describes the specific position and culture of the School of Criminal Justice of the University of Lausanne, Switzerland, in terms of proximity with policing organizations. It details the means developed over a century to build strong and fruitful relationships, such as shared PhD-professional positions, continuous education and training courses, collaborative student projects, or community-building initiatives. The advantages of such a close relationship, potential risks and limitations attached to it are discussed. The article advocates putting science and problem-solving at the crossroads of both worlds, and calls for an open-minded and optimistic approach of relationships between policing organizations and the university.
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 | 0.029 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.029 | 0.054 |
| Scholarly communication | 0.030 | 0.048 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".