Enablers, barriers, and future considerations for living lab effectiveness in environmental and agricultural sustainability transitions: a review of studies evaluating living labs
Bibliographic record
Abstract
Living labs are promoted as an effective open innovation approach that accelerates the adoption of innovations. However, there remain knowledge gaps about factors that influence their effectiveness, success, and application to sustainability transitions. Through a scoping review on the evaluation of living labs, we identified 43 enablers and 37 barriers to effectiveness and success of living labs organised around the themes of governance, processes, features of living labs, characteristics of participants, adaptability, social dimensions, training and research, technology, and elements beyond the living lab (e.g. conditions for transition to the real world). Key enablers included strong collaborative and iterative processes with networks and partnerships, while key barriers included issues with supporting technology, the time and cost of collaboration, and challenges ensuring the longevity of living labs. We also reviewed study objectives, knowledge gaps, and future considerations to identify priorities for future research about living lab effectiveness and provide recommendations for their implementation. We recommend the development of frameworks for measuring and monitoring the success of living labs, and explore other considerations to promote their effectiveness based on the enablers and barriers identified. Lastly, we discuss how our findings on living lab effectiveness and success related to this special issue. This paper contributes to the body of research by our team (Beaudoin et al. 2022; Bronson, Devkota, and Nguyen 2021) that aims to explore living labs in the context of conservation, environmental, and agricultural sustainability to facilitate transformative social-ecological change.
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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 | 0.061 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".