Bibliographic record
Abstract
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 (JRA). JRAs will also serve as the test bed for all the harmonization procedures, for assessing and improving the provided living lab services. JRA3 for everyday living environments will focus on evaluating the usage and applicability of innovative data collection technologies during the various everyday living activities executed inside and outside premises. The results will help to develop harmonized research protocol for developing big data–driven hybrid persona –hypothetical user archetypes created to represent a user community. This document presents the work performed for obtaining ethical approval for the research activities performed in JRA3. The results indicate significant ethical application process time differences between the countries ranging from few days to over two months. Three different types of the ethical boards were identified: internal, external and mixed boards. Internal entity referring to boards operating within the organization while external board is referring to a board which is not directly associated with the organization conducting the study. Mixed board includes both external and internal actors and can be managed by the research organization or external entity. All but one VITALISE JRA3 partner have submitted their ethical application. The missing partner (TREBAG) is submitting their application on April. Case studies incorporating AIT, LiCalab, CERTH, AUTH and McGill-UDEM have already received ethical approval. Laurea and INTRAS case studies were asked to do re-submission, which Laurea has already done while INTRAS is waiting for the Spanish Agency for Medicines and Health Products resolution on whether their study is considered as a medical device study before making the re-submission. GAIA has been waiting ethics board decision for over two months but should receive it on April. As a summary, it is concluded that JRA3 is progressing somewhat as planned, since additional information request are normal during the ethics process. The risk of delays that will have an impact on VITALISE project are not in sight.
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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 | 0.089 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.014 | 0.008 |
| Insufficient payload (model declined to judge) | 0.104 | 0.057 |
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".