Teaching towards social and ecological justice online: Introduction to Global Citizenship at UBC
Bibliographic record
Abstract
How can we help university students make connections between ‘academic knowledge’, and their roles as members of local and global communities? How do we create a forum for students to engage in issues of social and ecological justice through critical thought, moral commitment and meaningful engagement in their learning and coming to know as global citizens? We are an interdisciplinary group of researchers and instructors who have collaboratively developed, and are now co-teaching an international, interactive, fully online university course: Introduction to Global Citizenship, available to students at five universities around the world. Our course combines academic rigour with personal reflection and group discussion. It provides students with a broad understanding of barriers and bridges to global citizenship, brings greater awareness of key global issues, and encourages individual and collective action and accountability on issues of sustainability and social justice. Pilot delivery of our course in 2005-2006 suggests that it offers students an extremely challenging, thought-provoking, international educational experience, as we learn about and discuss global issues together. In this working session, we hope describe our experiences with this course project, and to facilitate a productive dialogue with colleagues around teaching strategies for transformative learning in higher education. What ‘kinds’ of transformative learning are we seeking and how can we recognize it? Which instructional strategies facilitate deeper critical analysis and personal reflection? What roles might technology and interdisciplinarity play in this undertaking? Which investigative approaches might help us move our institutions beyond lipservice to global education?
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.013 |
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".