CINECA Catalogue of Canadian, European and African ethical and legal gaps D7.2
Bibliographic record
Abstract
Remaining liberties that GDPR provides to EU Member States as well as remaining ambiguities on GDPR interpretation keep feeding the debates in the ethical and legal literature. Projects like CINECA, seeking to facilitate health data exchanges between cohorts in Europe, Canada and Africa, offer valuable experience and input on essential ethical and legal gaps between countries and cohorts on questions such as the choice of the ethical lawful basis for international health data sharing and secondary processing for research purposes. The focus of this deliverable will be on answering, both from a legal and an ethical point of view, two priority questions: How to choose a legal basis for CINECA’s data processing? And how should CINECA apprehend broad consent to further data processing? The goal will be to study how the CINECA project could be efficiently conducted (especially data sharing) while being legally compliant with relevant laws and regulations and most of all, being compliant with established ethical guidelines and practices across three continents.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.265 | 0.102 |
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".