International transfers of health research data following Schrems II: A problem in need of a solution
Bibliographic record
Abstract
On July 16<sup>th,</sup> 2020, the Court of Justice of the European Union issued a landmark decision in the case of <em>Data Protection Commissioner v. Facebook Ireland Ltd, Maximillian Schrems </em>(<em>Schrems II</em>) concerning the legality of Facebook’s transfers of personal data from the EU to the US. The decision has potentially significant effects on the ability of researchers to legitimately transfer personal data for health research purposes from countries inside the EU, to third countries outside the EU.<sup><sup>[1]</sup></sup> This article aims: i) to outline the consequences of the <em>Schrems II</em> decision for the legitimate sharing of personal data for health research between the EU and third countries, particularly in the context of the COVID-19 pandemic; and, in light of this elaboration, ii) to consider the avenues that might be pursued to remedy challenges posed by the decision and to facilitate international data exchange for health research moving forward.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".