Living Labs and Collaborative Innovation
Bibliographic record
Abstract
It is our pleasure to introduce you to this special issue in the Journal of Innovation Management (JIM) on the topic of Living Labs and Collaborative Innovation. Since their ``big bang'' with the establishment of the European Network of Living Labs (ENoLL) in 2006, living labs have been established worldwide to tackle an increasingly diverse variety of complex challenges such as urbanisation, agricultural sustainability and resilience, inequality, emerging technologies, etc. However, this surge in attention has been mainly driven by practice and policy, with the academic foundations lagging somewhat behind. With this special issue, we want to present you with an actual overview of current developments and achievements in living labs. Despite the wide variety of topics and domains in which they are being used and set-up, collaborative innovation is one of the main cornerstones of living lab practices. Therefore, there is a natural fit with the topic of Living Labs and Collaborative Innovation and the JIM, as it is an open access, multidisciplinary peer-reviewed journal, intending to publish cutting-edge research and findings on innovation and its management, bridging the gap between scientific research, policy making, and practice. (...)
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".