Living Lab as knowledge system: an actual approach for managing urban service projects?
Bibliographic record
Abstract
Purpose – This paper aims to explore Living Labs (LL) as knowledge systems for urban service projects. This empirical study aims to identify and characterize knowledge in LL dedicated to urban service projects. It also aims to understand how through knowledge path, LL redefine the management of projects. First, the praxeologic and academic context underlining the main challenges associated to urban service projects is presented. It mainly concerns the growth of the cities (Haouès-Jouve, 2013), the problematic of social acceptability (Savard, 2013) as well as the normative approaches to manage projects (Kerzner, 2010). Second, a literature review on co-innovation and Livings Labs is presented. (Chesbrough, 2004; Gaglio, 2011). This paper also presents the concept of knowledge applied in an LL system (Sanders and Stappers, 2008). Here, knowledge refers to dynamic knowledge, as suggested by Argyris (1995). Design/methodology/approach – In the third part, the goals of this study as well as the abductive and “partnership” qualitative methodology that was used are explained (Fontan and René, 2014). The constitutive and the operational definitions on knowledge that have been mobilized are detailed (Piaget, 1974; Gadille, 2012). A special focus is made, here, on distributed knowledge (Nowotny et al. , 2002; Trepos, 1996), on “users” as “experts of uses” (Chen et al. , 2010). Then, the sample and the four cases of LL that were explored are described. Findings – Finally, the findings are presented. This paper exposed how knowledge lying in the loops of the LL system was characterized and how knowledge is mobilized in an LL. This paper also draws a theoretical model of project management referring to knowledge, LL and co-innovation approach. Research limitations/implications – To conclude, several implications in project management research and urban studies are presented. Practical implications – Several implications concern the current practices of project management. Due to some new societal challenges, it is considered that a new professional posture is required. Social implications – Several implications concern citizens as users and stakeholders of urban projects. Originality/value – The originality of the study lies in its content and its format. A specific participative approach was used to explore LL. This paper investigated knowledge in LL, which are new entities dedicated to very actual projects, where users are co-managers.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".