INCULCATING SAFETY MANAGEMENT THROUGH AN ELECTRONIC LEARNING MODULE
Bibliographic record
Abstract
Inculcating a safety focused mindset is an important part of an engineering education, as highlighted by the Canadian Engineering Accreditation Board curriculum requirement to cover occupationalhealth and safety. Despite the importance of coveringsafety related material, many university faculty arehesitant to cover elements of safety management due tounfamiliarity with the material. In response, MinervaSafety Management Education has developed teachingmodule slide sets covering a wide range of safetymanagement topics that are freely available forinstructors to use in their classes.In this paper, we report on the next phase ofmodule deployment: the transformation of a slide set intoan electronically facilitated, self-contained, independentlearning module. This new format includes narratedlessons and automatic grading of comprehension tests.The format removes the time burden from instructors andteaching assistants, while enabling students to absorb thematerial at their own pace outside of class instructionalhours. The module is designed to be compatible withexisting learning management systems and easilyincorporated into existing course websites. We reportstudent feedback on this new format and share findingsapplicable to the development and conversion of futureself-contained electronic learning modules.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.013 |
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".