UNVEILING THE DIVERSITY OF SCHOLARLY DEBATE ON LIVING LABS: A BIBLIOMETRIC APPROACH
Bibliographic record
Abstract
Living labs (LLs) are becoming an increasingly popular approach to engage in open innovation. Although applications and influence of LLs have grown rapidly over the last decade, the landscape of LL research remains largely unclear and underexplored. Hence, there is an urgent need to develop a consolidated understanding of this research field and to detect the potential areas of fragmentation and isolation. Through a systematic review of the scholarly literature on LLs, this study applies bibliometric methods on a dataset of 411 journal articles. The results of this study reveal the diverse and fragmented nature of the LL field, with contributions spanning across different disciplines and application domains. Despite such fragmentation, some clusters of scholars and publications are identified as well as influential contributions. Given the nascent state of the literature, the role of special issues in shaping the evolution of the LL debate is prominent. This study provides a map to practitioners to investigate and learn from the application of LLs in diverse fields. This aspect is particularly important in light of the current COVID-19 pandemic, which stresses the key role of open and collaborative approaches to innovation, making the use of LLs increasingly relevant for governments, companies, public organisations and individuals.
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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 | 0.052 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.240 | 0.274 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.001 |
| 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".