The Frontiers of Service-Learning at Canadian Universities
Bibliographic record
Abstract
Service learning is a form of experiential learning that cultivates academic development, personal growth, and civic engagement. Students contribute to and learn from community. Service learning empowers students, enabling them to recognize their ability to act as agents of social change. Service learning is gaining momentum as a movement, given its ability to prepare students for the “real world” after graduation. The authors of this article come from health sciences, psychology, and environment and sustainability. Here, we illustrate service learning through four case studies: 1) An innovative team-based service-learning course partnering with older adults, healthcare providers and community agencies (Gerontology in Practice, Western University); 2) A unique curriculum design that includes service learning and interdisciplinary graduate problem-based training and research focused on experimental education (Environmental Sustainability, University of Saskatchewan); 3) An international service learning course that combines intensive coursework and a 3-month placement with a non-profit, community-based organization in Africa (Psychology and Developing Societies, University of British Columbia); and 4) An extraordinary example of an institutional-level commitment to service learning involving 50 courses, 40 faculty, 100 community agencies, and 900 students per year (St. Francis Xavier University). Our goal is to inspire other educators to engage in the pursuit of excellence in higher education through service learning.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.026 | 0.009 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.002 |
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".