Bibliographic record
Abstract
Despite the growing popularity of using living labs as an innovation platform, little is known about their characteristics.This research uses a case study approach with content analysis to identify a list of key attributes that help define the concept of living labs.Drawing upon the literatures on user innovation, co-creation, and living labs, theoretical constructs were used to guide the investigation of living lab applications that are publically available through the European Network of Living Labs (ENoLL) website.The results from the research produced nine constructs that give insight into living labs.These constructs reflect both qualified pre-existing notions and disqualified notions of living labs, such as the objective, the degree to which users are involved, and the legal structure for operations.They bring about significant new knowledge regarding the nature of living labs, including their modes of communication, their purposes, handling of intellectual property, and generation of funds.Advantages: The benefits the stakeholders gain from their membership and participation within the living lab.Coding: Technique used to look for distinct concepts and categories within the data.Component: An element of the innovative solution.Communication: The channels, technology and techniques used to network stakeholders for information exchange. Content Analysis:A method that enables a more objective evaluation of textual data, by counting the recording units versus comparing text based on impressions of a the researcher.(Emergent) Construct: An idea that enfolds various conceptual elements that is subjectively derived through observed evidence.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".