VITALISE D6.2 Ethical application documents for JRA2
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 industry 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 three Joint Research Activities will be implemented among the consortium Living Lab partners. These Joint Research Activities (JRAs) include state of the art use cases that investigate Active and Healthy Ageing (AHA) and chronic conditions in three important domains for the Health and Wellbeing Research. There were selected based on the consortium’s existing research studies and expertise: Rehabilitation, Transitional care and Everyday living environments (WP5, WP6, WP7). This document presents the work performed for obtaining ethical approval for the research activities performed in WP6, JRA2 Transitional care.
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 | 0.074 | 0.170 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.025 | 0.012 |
| Insufficient payload (model declined to judge) | 0.105 | 0.062 |
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".