How to Make Living Labs More Financially Sustainable? Case Studies in Italy and the Netherlands
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
In many urban environments, so-called Living Labs have been created. A Living Lab (LL) is an emerging innovation methodology that may serve to reduce the gap between new technology development and the adoption of this new technology by users, by bringing together all key actors in the innovation process: public administration, education institutes, companies, and citizens. However, a substantial number of LLs struggle to translate the customer value created into a sustainable business model. As a result, many LLs are financially not sustainable. Several previous studies found that most LLs primarily rely on public grants; thus, they often stop their activities when public funding ends. In this paper, we draw on a comprehensive literature review and practical evidence from three cases, to develop a framework of various funding options which can be employed by any LL that seeks to become more financially sustainable.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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