Moving toward Generalizability? A Scoping Review on Measuring the Impact of Living Labs
Bibliographic record
Abstract
The living labs (LLs) approach has been applied around the globe to generate innovation within and suited to real-life problems and contexts. Despite the promise of the LL approach for addressing complex challenges like socio-ecological change, there is a gap in practitioner and academic community knowledge surrounding how to measure and evaluate both the performance of a given LL process and its wider impacts. Notably, this gap appears particularly acute in LLs designed to address environmental or agricultural sustainability. This article seeks to verify and address this knowledge gap by conducting an adopted scoping review method which uses a combination of tools for text mining alongside human text analysis. In total, 138 academic articles were screened, out of which 88 articles were read in full and 41 articles were found relevant for this study. The findings reveal limited studies putting forward generalizable approaches or frameworks for evaluating the impact of LLs and even fewer in the agricultural or sustainability sector. The dominant method for evaluation used in the literature is comparative qualitative case studies. This research uncovers a potential tension regarding LL work: the specificity of LL studies works against the development of evaluation indicators and a universal framework to guide the impact assessment of LLs across jurisdictions and studies in order to move toward generalizability.
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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 | 0.218 | 0.546 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.035 | 0.028 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".