The Art of Jamming: Fast, Collaborative - and Possibly Transformative Action
Bibliographic record
Abstract
Jams - fast-paced, short-duration events - rooted in the improvisational spirit of jazz - have evolved into collaborative problem-solving arenas where diverse participants converge to tackle complex challenges with urgency. Some embrace their energy and immediacy, while others question their depth and sustainability, especially given the increasing need for slow moments in academia. We present two case studies from distinct geographical and disciplinary contexts: one from a UK Postgraduate Certificate (PGCert) program in Learning and Teaching in Higher Education (LTHE) and the other from a Design Masters course in Canada. We discuss the PGCert, illustrating how we act at speed in the classroom, creating many collaborative learning tasks delivered over a short, intense period of time: a series of fast-paced jams - where the reflection in and on action feeds into dialogic interaction and metareflection - and deep learning for the participants. In our Canadian example, the juxtaposition of a School of Architecture and Design with the city’s homeless served as a catalyst for asking critical questions about the School’s role in addressing social challenges. In Spring 2024, the School hosted a two-day Design Sprint involving 100 participants, including students, educators, municipal decision-makers, and frontline workers, to tackle the issue of homelessness in a short, powerful and influential period of time. This viewpoint article outlines what activities like this afford for education as well as the wider educational and social communities. We conclude with a provocation: is there a better way of naming and celebrating the power and potential of the jamming space in academia?
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.032 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".