International collaboration and connections through design thinking: A case study of the Global Classroom for Democracy Innovation (GCDI)
Bibliographic record
Abstract
This paper is based on the collaborative development of the Global Classroom for Democracy Innovation (GCDI), and its month-long virtual pilot workshop, the 'Climate Change Design Jam’. The GCDI is an integrated learning partnership between three international universities located in Canada, South Africa, and Sweden, and civil society partners the Vancouver Design Nerds (VDN). Each partner brought unique skills to the GCDI, as new processes and methods for virtual, global student engagement and dialogue were co-designed. The GCDI hosted the Climate Change Design Jam over four consecutive weeks in March 2022. By employing a design thinking methodology, it facilitated online student project development around the interconnected and broad topics of climate change and democracy. Students and student facilitators were guided through the process of design thinking to develop grounded projects that address climate change issues locally and internationally. This paper argues that fundamental principles of fostering genuine connections (both 'online' and 'offline') between students can act as a useful foundation from which project development can be based. Further, this paper illustrates that when faced with 'wicked problems’ such as climate change and challenges to democracy worldwide design thinking methods and collaborative approaches can act as a catalyst for action (Manzini, 2015). Exploring political theory, democracy, and civic agency through dialogue and co-design provides students with innovative approaches to research, critical thinking, and activism. This pilot series provides insight into student engagement across international contexts, and thus the development of cross-cultural and collective intelligence which can be formative for similar projects in the future (Behari-Leak, 2020).
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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 | 0.026 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.035 | 0.026 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".