A qualitative evaluation of sustainability-related courses at UBC
Bibliographic record
Abstract
[Conference Program Abstract] Climate and environmental changes are the most serious threat faced by our human society today. Teaching and learning about these issues are the most essential and crucial ways to not only investigate and solve these 21 problems but also increase awareness, monitor, and evaluate these ongoing challenges and propose alternative modes. Universities are looked upon as change agents, where both knowledge creation and knowledge exchange occurs. With the underlying theme of globalization and encouraging students to be global citizens, UBC adopted a sustainable development policy in 1997 and revised it in the most recent UBC Sustainability Academic Strategy (SAS) 2009. The new UBC SAS focuses on promoting sustainable practices through teaching, learning and research. UBC offers over 350 courses related to sustainability and has several research projects aimed at this issue as well. However, no research has been conducted to evaluate how and whether these courses are promoting sustainability, literacy about climate change, and citizen action among the students who take them. This study seeks to evaluate the impact of teaching and learning initiatives involving sustainability and sustainability-related courses through a qualitative approach, using focus groups and in-depth individual interviews. Further, the study is aimed to investigate students‘ understanding of these concepts and how they implement the strategies for environmental stewardship developed through these courses. Hence the question: How do the courses translate sustainability into sustainable actions or awareness?
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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 | 0.036 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".