euCanSHare. Deliverable D1.2 - Policy "Points to Consider" tool to guide research projects, policy makers
Bibliographic record
Abstract
The Centre of Genomics and Policy at McGill University has conducted an analysis of the ethico-legal requirements enshrined in data privacy law and research ethics guidance in Canada and the European Union. This Points-to-Consider document is intended to synthesize the elements of that research that are of relevance to the secondary use of health data by the cohorts of the euCanSHare project. In this summary, we have provided a general overview of our research. In Part 1, we assess the sources of the ethico-legal requirements discussed. In Part 2, we consider a number of regulatory requirements in the laws of Canada and the European Union. Elements discussed include legal prerequisites to data use, individual rights in data, and prerequisites to the international transfer of data. In Part 3, the identifiability of data, and the use of safeguards to protect data, are considered. In Part 4, the foregoing ideas are synthesized into holistic proposals for data governance. The conclusions of this Points-to-Consider document reprise the contents of recent and forthcoming academic publications that elaborate our findings in further detail.
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 | 0.027 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.522 | 0.411 |
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".