CINECA: Catalogue of ELSI issues_D7.1
Bibliographic record
Abstract
The goal of CINECA is to enable the exchange of population scale health data across international borders to allow and promote the reuse of data for health research. The rationale for sharing and reusing data in public health research is deeply rooted in the promotion of a fair distribution of research risks and benefits, and it has become an essential and powerful tool for public health research. In pursuit of this goal, this deliverable aims to give an overview of all the different ethical, legal and societal issues that the CINECA project might be confronted with: public health ethics, personal data protection, ethics of data sharing, protection of consent and vulnerability as well as compliance issues between Canada, Africa and Europe. It has been elaborated in a bottom up approach, starting from the practical legal and ethical issues encountered notably through Work Package 9 (EC Ethics Requirements). As a basis for the lawful and ethical guarantees for data sharing and reuse within CINECA, all cohorts and consortiums have provided for the copies of their own ethics approvals (Deliverable 9.4), and they are all independently responsible for ensuring researchers accessing data have their own research ethics approval. This deliverable will serve as a starting point for the future deliverable 7.2 which will be aimed at identifying and discussing the gaps in the different legislative or regulatory frameworks and corresponding literature. As a consequence, this deliverable is divided into two main parts, the first one focusing on the collective perspectives of international data sharing in public health research, the second one examining the opposite perspective of the protection of individual data subjects when their personal data is used for secondary processing.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.553 | 0.452 |
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".