Governing University Living Labs for Sustainable Development: Lessons from International Case Studies
Bibliographic record
Abstract
Funded by the Monash-ENGIE Alliance, this report outlines key recommendations and drivers to help University Living Labs stimulate innovation towards collective action on global challenges like climate change. University Living Labs are an important vehicle for real world experimentation and learning because they bring together community, government, business, NGOs, and researchers on and off campus to work towards common goals for sustainable development and other societal challenges like sustainability, which are beyond any one group to tackle alone. Living labs involve university-industry partnerships, experiential learning for students, use of the campus as a testbed, and knowledge translation and commercialisation for social impact. This study identified shared challenges and critical success factors for enabling these initiatives by speaking with academics and practitioners involved in 18 University Living Labs across Australia, Brazil, Canada, France, Germany, The Netherlands, Singapore, the UK, and the US. This report is the first international comparative study of University Living Labs focused on how these initiatives can be organised and embedded in universities. The study highlights that while University Living Labs take a variety of forms, they often face common challenges associated with insufficient governing structures, ad hoc funding, siloed institutional cultures, and a lack of shared understanding. The report makes practical recommendations for overcoming these challenges through flexible coordination, investment, facilitation, and communication. The report was co-authored by Dr Paris Hadfield, Dr Darren Sharp, Sam Rye, and Prof Rob Raven from Monash Sustainable Development Institute and Dr Mures Zarea, Dr Jonas Pigeon, and Dr Xiaoyang Peng from ENGIE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".