Bibliographic record
Abstract
What does it mean to be a “responsible engineer”? The question has been asked throughout history, including during times that were fraught with challenge, but the problems awaiting engineering students in Canada in 2021 are arguably unprecedented in their complexity. Technologically difficult problems resulted in recent advances such as the World Wide Web and 5G mobile communications, but today’s challenge derives not only from the global scale of man-made environmental problems and their impacts, but from lack of agreement around appropriate mitigation strategies and on the need to act at all. The engineering students of today must develop solutions to problems that have newly acknowledged and highly contentious human factors. In the Canadian context, new policy is emerging from Canada’s recognition that it must reconcile the harms of past policies towards Indigenous peoples. This recognition has created a discourse around equity that will shape the landscape in which graduates will practice. In preparing young engineers to navigate technological and social complexity, and to equip them to find personal fulfillment in an uncertain landscape, a reinvigorated emphasis on critical and relational thinking is required. In 2019 and 2020, students in a fourth-year class in Sustainability, (which includes a study of law, policy, and sustainability reporting metrics) participated in multiple group exercises that asked them to explore multiple facets of problematic or politically current issues. These learning activities sought to support a transformative learning experience in which students would come to recognize their ability to contribute as citizens to the development of responsible public policy. In these exercises they examined opposing positions and assessed the strengths, weaknesses, opportunities and risks, associated with each. That there was no RIGHT conclusion was emphasized. The only criteria for assessment was depth of exploration and logical chaining of evidence. Students learned about cultural perspectives from an Indigenous educator, and as they explored the diversity of viewpoints around the question of pipeline expansion in Canada. A final exercise asked students to reflect on the meanings of responsible policy and practice and how they would operationalize sustainability thinking.
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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 | 0.050 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.127 |
| Scholarly communication | 0.027 | 0.030 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.010 | 0.020 |
| 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".