Living labs as transformative incrementalism: lessons learned on the role of a university living lab in mobilising just sustainabilities on campus
Bibliographic record
Abstract
The Living Lab approach is an opportunity for diverse actors to co-create solutions to solve real-world issues. Simon Fraser University, situated on the unceded territories of the Musqueam, Squamish, Tsleil-Waututh, Katzie, Kwikwetlem, Qayqayt, Kwantlen, Semiahmoo and Tsawwassen peoples in British Columbia, Canada developed a Living Lab program to apply the university’s leading climate research expertise to solve its own infrastructure, operations and service challenges. Projects were led by “Living Lab Scholars,” graduate students who form teams with faculty and staff to co-design research to help the university meet its sustainability and equity goals. The scholars took part in experiential learning, received mentorship and financial support and were provided with the opportunity to apply their academic research skills to address four sustainability issues: (1) waste management, (2) sustainable transportation, (3) carbon footprint of streaming, and (4) food security. While being grounded in participatory action research and integrating justice, decolonisation, equity, diversity and inclusivity considerations into the process design, the limited resources, time scarcity and operational reality reflected that the reality of implementing the solutions resulted in varying degrees of transformational impact. This paper applies autoethnography to enable the participants to reflect upon how the university as a system can support advances in just sustainabilities and highlights practical lessons learned for future Living Lab practitioners who aim to mobilise their solutions on campus. Findings from the project highlight the role of the Living Lab in supporting “transformative incrementalism” and challenging the conventions of academic knowledge production.
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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 | 0.022 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".