Two-Eyed Seeing: An ethical space of engagement to shape engineering and computing education for sustainable development
Bibliographic record
Abstract
Universities serve as institutions for acquiring knowledge and instilling values in the learning environment, including students. These extend from the preventive moral values developed in schools to the strategic aspirational sustainability values to enhance competencies including knowledge, skills, attitudes, and ways of seeing and being worldwide. This paper is about a sustainability research experience as part of an undergraduate course on professional practice offered to engineering and computing students at the University of Ottawa, Canada. The course employs a "learn-by-research" approach in developing cases and projects. The experience highlights the significance of aspirational ethics in creating a distinct space of engagement steered by sustainable development as a core value for empowering and not overpowering society. This space is guided by the Two-Eyed Seeing principle which helps to engage the powers of Indigenous and Western knowledge for learning and practice. A broader Two-Eyed Seeing perspective to shape engineering and computing education was interpreted and employed. The students' anonymous survey and semi-structured interviews revealed noticeable improvements in their understanding, skills, and competencies toward sustainable development. This integrated approach to curriculum and pedagogy fosters critical and creative thinking in learners and cultivates a growth mindset that empowers them with research skills and sustainability knowledge. The outcomes of the study may act as an informing catalyst where human values and society are at the core to facilitate a transition in education for sustainable development .
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.055 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.005 | 0.008 |
| 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".