Participation of elderly rural citizens in the Living Lab MOSAIC: Protocol for a longitudinal mixed-method study (Preprint)
Bibliographic record
Abstract
BACKGROUND The third generation of living labs could strengthen innovation and the development of broad social effects. Since August 2022, the rural areas of Chaudiere-Appalaches in Quebec, Canada, have been the site of the living lab MOSAIC. Collaborators involved in this living lab include four stakeholders: seniors and their families, representatives of the community, representatives of service providers, researchers. A facilitating team organizes the daily life around the living lab. The success of MOSAIC depends on the involvement of rural elderly in the co-creation, implementation, and sustainability of social innovations. Little is known on how to facilitate the participation of rural elderly in living labs. OBJECTIVE Our objectives are to offer a better understanding of the participation of rural elderly in the development of an intersectoral rural living lab, to explore the enablers to and challengers of older citizens' full participation in the living lab process, and the adaptations put in place to facilitate their participation. METHODS This is a longitudinal mixed-methods study. We will use scientific methods to make sense of diverse sources of secondary and primary data. We will also conduct semi-structured individual interviews at the end of years 1 and 2 and lengthwise observation. Participants will be the collaborators already involved in MOSAIC as well as the facilitating team. RESULTS This study will lead to new findings on the experience of rural seniors participating in a living lab. Additionally, this project will enhance knowledge of the human dynamics involved in establishing a living lab project with elderly rural citizens as well as its implications for epistemology, methodology, and ethics. CONCLUSIONS Participation of end-users, often rural lay persons, are the terms and conditions of a lot of research and development methods increasingly regarded as valuable. This study will result in fresh insights and cutting-edge knowledge on the challenges faced while co-creating and implementing social innovations with the elderly.
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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 | 0.070 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.053 | 0.015 |
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".