Two-Eyed Seeing: An ethical space of engagement to shape engineering and computing education for sustainable development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 .
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it