THE FOURTH INDUSTRIAL REVOLUTION, HUMAN SKILLS, AND ONLINE LEARNING: NOTES FROM THE HIGHER EDUCATIONAL EXPERIENCES OF POLICE OFFICERS
Bibliographic record
Abstract
This article describes how the blended format of a higher education program initially designed for police officers supported the nurturing of human skills, such as creativity, collaboration, problem solving, and critical thinking, which are increasingly seen as essential in 21st century workplaces. The article begins with a discussion of the Fourth Industrial Revolution and how the impacts of automation, artificial intelligence, and the Internet of things, among others, are compelling reevaluation of non-cognitive or human skills. Then, the article explores the evolution of the educational program at the University of Guelph-Humber, in Toronto, Canada, and outlines how by-products of the program were hallmark liberal arts skills, such as critical thinking, research, analysis, and communication, which were seen as valuable to police work. Third, it highlights some of the specific impacts of increasing technological inputs such as artificial intelligence, crime mapping, and predictive policing algorithms on everyday community policing. Finally, the article correlates how, perhaps counterintuitively, blended or hybrid and online education can create learning environments to nurture the human skills to respond to the workplaces of the future.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".