International data-sharing norms: from the OECD to the General Data Protection Regulation (GDPR)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The evolution of genomic research and its integration into clinical practice, as they become international-even global-endeavors, has brought us to a place where scientists and clinicians may now only ignore the rules governing international data sharing at their own peril. Open data policies, on the one hand, increasingly require custodians of others' genomic data to make it as widely available as feasible, including to researchers in other countries. Data protection law, on the other, has become a significant hurdle to the sharing of personal data across jurisdictional borders. The space between these two competing duties is narrowing. In contrast with the other texts in this volume, which explore the present and future of data sharing and data protection, this article's focus is on the past. It centres on the historical development of the data protection rules regarding the international transfer of personal data up to the present. The article's aim is to bring into focus the underlying objectives that have influenced and that will continue to influence the way that data protection rules are applied to the fields of genomics and health, as well as future developments in data protection generally. The first part of this article describes the development of international data-sharing data protection rules since 1970. The second considers difficulties in applying general data protection rules to the specific context of genomics and health. The third and final part compares the options available to comply with the international transfer restrictions set out in the standard-setting EU General Data Protection Regulation from a genomics perspective.
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.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it