Enablers, barriers, and future considerations for living lab effectiveness in environmental and agricultural sustainability transitions: a review of studies evaluating living labs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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. Citation2022; Bronson, Devkota, and Nguyen Citation2021) that aims to explore living labs in the context of conservation, environmental, and agricultural sustainability to facilitate transformative social-ecological change.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it