Bibliographic record
Abstract
Many discussions of health information privacy in relation to biobanks focus on the question of informed consent and, in particular, on why the future of biobanking likely requires the acceptance of some derogation from the traditional standard of informed consent to medical research. Following Neil C. Manson & Onora O’Neill’s recent work on informed consent as a “waiver,” we argue in this paper that the role of consent can be thought of in a narrower but still highly principled manner which is consistent with how consent is dealt with in tort law. Using this conception, we defend the position that the traditional standard of informed consent to participate in a biobank can be met but only where there is a governance structure ensuring consistent information practices and policies across multiple future research projects. We also argue that specific research uses of research samples and associated data in the biobank do not necessarily require informed consent but do require a governance structure that can regulate privacy risks such as the risk of re-identification. We thus distinguish consent at the outset of the collection and no-consent for future use, with both requiring a governance structure in order to protect the important interests at stake. In our conclusion we suggest that the legal and ethical debates regarding biobanking must shift from an obsession with consent and focus more closely on the elements of good governance in order to move away from compromises and back to the principled protection of research subjects.
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 | 0.056 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.097 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".