A Scoping Review Examining Governance, Co-Creation, and Social and Ecological Justice in Living Labs Literature
Bibliographic record
Abstract
Living Labs (LLs) are increasingly being used as an approach to address complex sustainability-related challenges. Inspired by existing knowledge and practice gaps, calls for further examination of governance and co-creation in relation to LLs work, and our experiences in the Lake Superior Living Labs Network, we conducted a scoping review of the recent (2015–2019) LLs literature. This review focused on peer-reviewed LLs literature aimed at addressing sustainability-related challenges and involving universities as key collaborators specifically. This scoping review addressed the research questions: how are LLs conceptualized, described, and applied? how are LLs governed? How is co-creation supported in LLs work? and, are social and/or environmental justice considered in LLs work? From the 729 citations gathered in the electronic database searches, 48 papers were identified as relevant through the screening and eligibility assessment. We found that this literature is growing rapidly, highly interdisciplinary, and predominantly taking place within European urban centres. We summarize the findings in relation to our research questions and outline implications for interrogating governance, unpacking co-creation, and working towards social and ecological justice in LLs research and practice. We conclude by outlining four key research directions to advance LLs work, including, (1) expanding research across a greater diversity of settings; (2) examining and analyzing governance and power dynamics; (3) exploring how learning evolves via co-creation; and (4) examining how universities are impeding and/or supporting advances in relation to governance, co-creation, and justice in LLs work.
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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 | 0.019 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.035 | 0.038 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| 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".