TEACHING DESIGN FOR SAFETY THROUGH REAL WORK INCIDENTS
Bibliographic record
Abstract
Good design means at least safe design. There is no doubt that engineering students have to develop the ability to design for safety in order to satisfy the expectations of the profession and of employers. According to the Canadian Engineering Accreditation Board, an appropriate exposure to safety and health must be part of any engineering curriculum. Design for safety principles can be taught in project-based courses. Those courses provide real-world contexts for the development of design for safety skills. Design minima established in standards, regulations, and handbooks can easily be integrated throughout the curriculum in engineering science courses. In association with safety, engineering science courses may go beyond the application of standards and guidelines and prepare the student to become a better designer. The Case Study approach is a very interesting pedagogical tool for engineering education and can be introduced in the most engineering courses. When based on real work incidents and investigation reports, case studies become a perfect instrument to expose students to safety as professional engineers should apply it. To illustrate the idea, this paper presents an example of a case study based on an incident that occurred with a scissor lift. This particular case was introduced in a machine design course and helped students to link safety concepts with machine design contents.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".