Bibliographic record
Abstract
This paper proposes a research agenda for Living Labs as after 20 years of Living Lab-practice, theory building is not up to par with the steady growth and expansion of practice in terms of topics, sectors and sheer number of practitioners and initiatives. By means of a literature review, a workshop with the Living Lab research community (N=60) and a self-assessment of Living Labs on key topics (N=70) we were able to identify the following main action points to be tackled by future Living Lab research: 1. Linking the different levels of Living Labs to existing theories and literature streams; 2. Approaches to increase impact, value creation & scaling of Living Labs; 3. Harmonization & differentiation of Living Labs; 4. Long(er)-term user & stakeholder involvement; 5. Inclusive involvement. Especially in terms of impact, there is a big demand from practitioners’ perspective which could be addressed by first emphasizing the harmonisation process of the different Living Labs elements and terminology and subsequently theorising and conceptualising the current diversity in terms of themes and topics.
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.
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 | 0.100 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.028 | 0.054 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".