Exploring the design and contributions of urban agroecosystem living labs for sustainable city development
Bibliographic record
Abstract
Living Labs are open innovation ecosystems within real-life settings, designed to address societal challenges through iterative feedback processes. In the context of promoting sustainable cities, the urban agroecosystem living labs (UALL) approach has garnered increasing attention. This study investigates how UALLs are designed and what their contributions are to promoting the sustainable transition of cities. A content analysis was conducted on UALLs identified in both academic and grey literature, examining their approaches and benefits. This was achieved by mapping UALLs identified in both academic and gray literature. Subsequently, a qualitative assessment of their design was conducted, focusing on their aims, processes, activities, and participants. Their alignment with the Sustainable Development Goals (SDGs) and their contribution to urban sustainability were also explored. The study identified 34 UALLs, which exhibit a remarkable diversity in their design. Besides boosting agricultural productivity and food security, UALLs help shape sustainable cities by promoting responsible food consumption, enhancing community cohesion and resilience, improving greening and biodiversity, supporting climate mitigation, and reducing waste. The most notable UALLs were the AU/LAB Centre for Co-creation and Innovation Opened for Urban Agriculture in Canada, ILVO Living Lab Agrifood Technology in Belgium, and the RUBA Living Lab in Colombia, with holistic approaches that address ecological, economic, and social aspects of urban environments. However, UALLs must align their strategies with the SDGs and strengthen the measurement of their contributions. This paper encourages broader adoption of UALLs, improving its design while expanding its influence in the pursuit of sustainable cities.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".