Survey of approaches for including the impact of technology on society in canadian engineering undergraduate curricula
Bibliographic record
Abstract
Increasingly, engineering educators are called on to include the technology-society interface in undergraduate engineeringeducation, but little consensus exists on precisely how to present or treat technology and society in the classroom. However, it isgenerally recognized that the topic should be taught from a multidisciplinary or interdisciplinary perspective and integrated into theengineering curriculum. Within Canada, engineering programs are required to complement technical content with severalcomplementary studies topics, including impact of technology on society. Canadian engineering programs have generally required asingle, stand-alone course on each complementary study topic. Recent changes to the accreditation requirements seem to indicatean increased emphasis on the technology-society interface as well as the need to integrate the topic across the curriculum. Wecharacterize current approaches for fulfilling the impact of technology on society requirement and discuss the disciplinaryperspectives used by Canadian universities. Our findings indicate that many are still meeting the requirement solely with a stand-alone course. However, several have made progress toward more fully integrating impact of technology on society into thecurriculum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".