Overlap of Ethics and Sustainability for Responsibility Mindset: Insights for an Educational Approach Change in Ethics of Engineering
Bibliographic record
Abstract
The mandate of universities and higher education institutes is to shape students’ ethical wisdom and catalyze sustainable development. However, everyone does not have equal access to higher education which drives the above mindsets, leading to unequal quality of education. Today, most ethics teaching in engineering schools is narrowly micro-ethics and does not attempt to define macro-ethics’ special challenges. Micro-ethics concentrates on concerns related to the individual and the innermost workings of the profession. However, macro-ethics encompasses sustainable development with a focus on the collective social responsibility of the profession and public concerns about technology. This study examined undergraduate engineering programs in 25 major Canadian universities to see if they are adequately prepared to navigate both the micro- and macro-aspects of ethics education. The curricula of the programs show many courses focusing on micro-ethical concepts. The lack of explicit macro-ethical agendas for advancing society invites further work on curricula to enhance the efficacy of the content and delivery modes. This inadequate commitment to socially responsible engineering is conceptualized as a “culture of disengagement”. The study introduces important moments of change to strengthen ethics education. These include twinning engineering and computing ethics; embedding overlapped ethics, sustainability, and responsibility mindsets; and promoting the scholarship of integration through research, synthesis, practice, and teaching. By adopting these approaches to curriculum and pedagogy, the students will effectively cultivate a critical understanding of and obligation to the profession’s collective responsibility and the well-being of the greater society. These changes provide crucial insights into broadening socio-technical-minded prospects of engineering ethics education.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.074 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".