Innovation in Living Labs: A Quantum Approach
Bibliographic record
Abstract
Living lab research is a well-accepted stream of innovation management literature. Although previous research has documented living labs from a variety of perspectives, the core of living labs and their principles remain largely underexplored. The present study analyses innovation in living labs inspired by the lens of quantum theory and its key concepts, including superposition, entanglement and wave function collapse. More specifically, the study applies insights from quantum theory to improve our understanding of innovation endeavours in living labs. The framework developed in the study illustrates how and why living labs advance innovations: they enhance collisions of individuals with different backgrounds and knowledge, thereby increasing potential realities (superpositions) and their collapses. The study contributes to living lab literature by suggesting that living labs can be seen as a realisation of quantum computing in real-life environments, speeding up innovation activities. While the study explores conceptual aspects, its findings can offer valuable insights for policy makers and practitioners engaged in living labs.
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How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.022 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".