Aspects of Changing the Safety Culture in Today's Universities: 2013 Process Safety Management Award, Canadian Society for Chemical Engineering
Bibliographic record
Abstract
The University of today is little changed from that of a century ago. Students attend lectures, sitting in rows of chairs rather than benches. The lecture notes are projected onto a screen; only occasionally are chalkboards used. The biggest change is with technology, in that lecture material is posted to a course website; a growing number of lectures are videotaped so that students can review the lecture whenever they wish. The pressures on universities come in the form of mandated quality programs. Programs are expected to define what it is that they expect their students to learn along with having a system that measures student learning, and a review process that identifies deficiencies and proposes solutions. Engineering programs are very good at imparting knowledge; these are the mathematics, natural science, and engineering science components of the curriculum. Programs are less successful at the industrially relevant aspects of the profession. The criteria are described in accreditation criteria, but there is no generally accepted curriculum model. All programs provide courses in thermodynamics, fluid mechanics, and reaction engineering, but many have no similar course on the management of safety. Further, the requisite “industrial knowledge” is not widely held within the university world. This paper provides a historical perspective on this issue, reviews attempts to improve the situation, and proposes improvements to address the situation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".