Towards innovation in Living Labs networks
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
This paper focuses on Living Labs that are open user-centred environments for networked innovation development. Although the concept of open innovation has quickly attracted both the scientific and applied communities, research on Living Labs is scarce, and literature lacks understanding of the characteristics of the Living Labs model. We aim to describe what the Living Labs are from the innovation network perspective. Using a case study of a regional Living Labs initiative, we describe the key participants and their roles in the Living Labs network. In addition, we discuss their motives to participate in the network, as well as the outcomes and perceived challenges of innovation co-creation. According to our findings, Living Labs are a practical way of encouraging open innovation. They are dedicated inter-organisational environments that provide pertinent support for Concurrent Engineering’s (CE) networked processes. The integration of users as co-producers in product development is imperative for success in the Living Labs model because it reveals their latent needs and enables unforeseen outcomes.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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