Collaborative Sustainability Research Experience for Unleashing Inclusivity and Equity in Engineering Education
Bibliographic record
Abstract
Employers are highlighting the importance of knowledge and professional skills, including personal, interpersonal, communication, and thinking, in their quest for graduates who are prepared for the workforce. Collaborative research is an essential toolbox that integrates knowledge, skills, and attitudes, which is important for future engineers; nonetheless, undergraduate students often struggle to engage effectively in this key competency. This study presents an undergraduate sustainability research experience (CUSRE) that is built into two courses, utilizing a collaborative-based learning (CBL) setting aimed at creating knowledge, improving skills and competencies, encouraging inclusivity, and advancing equitable education. The objective of the study is to narrow the achievement gap, improve graduation rates, and boost students’ enthusiasm and readiness for the Sustainable Development Goals (SDGs). It encompasses a strategy that integrates key approaches, including collaborative research, sustainability as a core value and practice, and educational equity supported by compensatory pedagogy that emphasizes teamwork. Introduced at the University of Ottawa (uOttawa) in Canada, the initiative engaged students to deepen their understanding of the SDGs through research cases and projects. This experience yielded significant knowledge gains and a considerable success rate among participants. Moreover, it has been successfully scaled and adapted for the Global Banking School (GBS) in the UK, thereby broadening its impact to a larger audience.
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 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 | 0.014 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".