A Sense of Giving Back: Sustainability Lens in a STEM Program
Bibliographic record
Abstract
Experiential Learning, Sustainability Focus, and a Sense of Giving Back offer a critical intersection for innovative solutions, increasingly understood in the education sector. This study presents a novel approach to integrating sustainability into STEM education through volunteering and community-focused projects. Over a three-year period, we studied a program at a Canadian University’s Computer Science department and interviewed 26 participants. During this time, the program developed 24 projects involving 84 students, in collaboration with 16 non-profits and 25 industry-mentors. This sustained initiative enhanced STEM learning by tackling global challenges such as education, health, and community development in Canada and Nepal. The findings highlight key design elements and roles of volunteering for such initiatives that foster equitable, cross-cultural collaboration and sustainable community impact. The unique combination of digital solutions and local knowledge, coupled with a diverse network of digital volunteers, demonstrates the program design's significance in advancing both educational and social outcomes.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.015 | 0.029 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.005 |
| 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".