Forensic intelligence teaching and learning in higher education: An international approach
Bibliographic record
Abstract
Over the years, forensic science has primarily positioned itself as a service provider for the criminal justice system, following the dominant and traditional reactive law enforcement model. Unfortunately, this focus has limited its capacity to provide knowledge about crime systems and to support other forms of policing styles through forensic intelligence. Although forensic intelligence research has steadily developed over the last few years, it is rarely covered in the core of academic teaching and research programs. Developing forensic intelligence programs would empower graduates with an awareness of forensic intelligence meaning and models, creating great opportunities to shape their future professional activities and progressively shift the dominant paradigm through a bottom-up approach. In this article, the teaching and learning strategies in forensic intelligence developed at the University of Lausanne (Switzerland) and adapted at the University of Technology Sydney (Australia) and the Université du Québec à Trois-Rivières (Canada) are presented. The objective behind the strategy is to reflect on and work on real case scenarios using a progressive teaching and learning approach that builds upon the theory and practical exercise putting students in real-life situations. Through this innovative learning process, students move away from the Court as the sole end purpose of forensic science. They learn to adopt different roles, adopt a proactive attitude as well as work individually and collaboratively. This teaching and learning strategy breaks the current silos observed in the forensic science discipline by focusing on processes and critical thinking. It can be foreseen, through the evolution of crime and policing models, that the learning and teaching strategy described in this article offers and will offer the students with many new job opportunities. The article concludes with the advantages that such teaching and learning programs in forensic intelligence bring to the forensic science community.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".