VITALISE D5.3 Summary of the performed activities for JRA1
Bibliographic record
Abstract
Although Living Labs have emerged as resilient research and innovation infrastructures and have proved to be a “key” to the integration of research and innovation processes in real-life settings, they still fail to provide and function according to unified and harmonized processes that are easily accessible and exploitable by academic and industrial researchers. VITALISE brings together Living Labs across Europe (and 1 outside Europe in Canada) to create a Thematic ecosystem of Living Labs in the Health and Wellbeing domain, aiming at creating synergies and transnational collaboration opportunities through innovative Joint Research Activities. During VITALISE project three Joint Research Activities (JRAs) were implemented among the consortium Living Lab partners. These JRAs included state-of-the-art use cases that investigated AHA and chronic conditions in three important domains for the Health and Wellbeing Research. They were selected based on the consortium’s existing research studies and expertise: Rehabilitation, Transitional care and Everyday living environments (respectively JRA1, JRA2, JRA3). The JRA of WP5 focused on the use of supportive technology for rehabilitation interventions and data collection in a rehabilitation context. We primarily aimed to gain insight in each living lab’s infrastructure and procedures in order to harmonise health and wellbeing living lab procedures and infrastructures in Europe and beyond, in particular in the context of rehabilitation. Secondly, we aimed to investigate the potential of innovative technologies for rehabilitation through living lab methodologies. Six small-scale pilot studies were preceded by co-creation sessions. This document presents an overview of the case studies performed in different countries.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".