Living Labs (as Intermediary Organizations) and the Phenomena of Inclusion: Not an Easy Journey
Bibliographic record
Abstract
The emergence of Living Labs (LL), now present on several continents, has given rise to a large body of academic and practionnal works over the past decade. Leminen and Westerlund (2019) have been able to trace this movement since its emergence in the early 2000s. The phenomenon of inclusion is cited in many papers as the crux of a Living Lab, which allows this organization to generate a 'relevant' common good. However, very little research has explored this phenomenon. This qualitative study aims to define inclusion in LL and its contribution, as well as the challenges associated with inclusion in LL. The study attempts to describe these dimensions by first identifying what inclusion means in the context of some urban LLs. The findings show that inclusion in LL is about knowledge, stakeholders and social inclusion. It brings individual and collective benefits, and knowledge sharing and perceptions of inclusion are among the challenges to be overcome. The study is exploratory, based on a broad review of recent cross-literature as well as secondary data and expert feedback. The approach is both theoretical and empirical. Data processing is carried out through cross-checking and grouping.
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 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 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".