Governing University Living Labs for sustainability transformations: insights from 18 international case studies
Bibliographic record
Abstract
Abstract In recent years, scholarly debate has grown around a perceived gap between societal impact rhetoric and the support structures for interdisciplinary and applied research, education, and innovation in universities. University Living Labs (UniLLs) provide a window into this relationship as they transcend disciplinary boundaries and linear modes of engagement to enable real-world experimentation and learning in response to societal challenges such as sustainable development. However, few studies examine the institutional contexts in which UniLLs operate, thus limiting our understanding of universities’ capacity for sustainability experimentation. This study examines how the institutional structures, cultures, and practices of universities enable or constrain the governance of sustainability-oriented UniLLs. Our study is grounded in the practical work of organising and conducting UniLLs, drawing on interviews with 39 academics and practitioners involved in UniLLs at 18 universities in Australia, Brazil, Canada, Germany, France, The Netherlands, Singapore, the UK, and the USA. Our research findings demonstrate that (1) UniLLs are enabled by the institutionalisation of sustainable development agendas, and the relational and discursive work of key university staff. (2) UniLLs are often limited in scope and longevity by a project logic and work against entrenched academic and operational organising structures and corporate logics. (3) Some UniLLs overcome these barriers by leveraging institutional power and mobilising resources to embed UniLL governance in university-wide structures. We present practical enabling processes for institutionalising UniLLs in universities demonstrated by the cases and reflect on the university governance paradigm for advancing a transformative impact agenda.
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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".