HOW FINE ARE THE EMPEROR’S CLOTHES? – MOTIVATING CRITICAL AND ETHICAL DESIGN PRACTICES BY DECONSTRUCTING ENGINEERING CODES AND STANDARDS
Bibliographic record
Abstract
Abstract – At the University of Toronto, Engineering Science students are typically introduced to the engineering codes and standards that they are expected to incorporate into framing and responding to engineering design challenges in their first year of study. In our experience, however, students do not always appreciate that these codes and standards may not reflect the interests of key (and potentially under-represented) stakeholders, and thus may not be appropriate for their engineering context. To encourage our students to adopt a more critical perspective when working with codes and standards, we exposed them to case examples of contentious regulations, and highlighted the objectives, people, and processes behind the development of these works. Our examples focus on common products to which first-year students can relate, such as handrails and stairs. By exposing our students to the people and processes by which codes and standards are developed, and to the controversies associated with contentious policy decisions, we expect that students will adopt a rigorous approach to using engineering codes and standards in their design activities.
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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 | 0.026 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.034 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".