International Guidelines for Privacy in Genomic Biobanking (or the Unexpected Virtue of Pluralism)
Bibliographic record
Abstract
This article reviews international privacy norms governing human genomic biobanks and databases, and how they address issues related to consent, secondary use, de- identification, access, security, and governance. A range of international instruments were identified, varying in substance - e.g., human rights, data protection, research ethics, biobanks, and genetics - and legal character. Some norms detail processes for broad consent, namely, that even where potential participants cannot consent to specific users and uses, they should be given clear information on access policies, procedures, and governance structures. Some also give guidance about the conditions under which secondary use of data and samples without consent is appropriate, e.g., where consent is impracticable. International norms exhibit a confusing range of terminology relating to de-identification. They also continue to rely heavily on consent and anonymity as the basis for privacy protection, though governance is becoming more prominent. It may not be fatal that such a plurality of norms apply to biobanking; what is essential is that governance be built on shared values, our common interest in the success of genomic research, and practical tools that incentivize responsible, global sharing.
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 | 0.042 | 0.206 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| 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; both teacher heads agree on what is shown here.
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".