Police learning: examining the use of simulations in police training and the associated learning theories
Bibliographic record
Abstract
In addition to the cost savings, technology such as e-learning, hybrid learning and simulations are tools that are used to equip police force with the competencies and skills needed to protect the public and respond to the changing demographics. That is, the future of police training will be geared towards developing competencies to support existing training for police and the recruitment process that relies on technology and integrated crisis management systems. Simulations, robotics and reallife situations are vital in preparing the first responders and decision makers with the skills and competencies needed to respond effectively to a crisis. The successful design of the training strategies will provide police force with tactics that will enable them to offer the training in a suitable delivery medium. In this paper, we explore the use of simulations and virtual environments in police training, investigate examples of integrated simulation architectures and list how the various learning theories apply to police training. While there has been many research done around the use of simulations and virtual training in police force training, only very few provide information about the use of integrated simulation architecture and the application of learning theories in their training. Thus, comes the novelty of this paper. Keywords: Virtual, Simulation, Police, Learning Theories, Public Safety, Crisis Management
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".