Broad Consent in Biobanking: Reflections on Seemingly Insurmountable Dilemmas
Bibliographic record
Abstract
Biobanking has long generated a host of challenging ethical and legal issues, particularly in relation to consent. A topic at the heart of current debates is the acceptability of the use of ‘broad’ consent in place of more specific and conventional consent approaches. In this paper, the justifications for the move toward broad consent are analysed and critiqued. It is concluded that all of the justifications-such as concern for costs, the desire to do research in the public good and the concept of minimal risk—remain contentious and seem insufficient, at least on their own, to stand as a rationale for a move away from traditional notions of consent.
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 | 0.217 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.188 |
| Scholarly communication | 0.027 | 0.045 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.052 | 0.066 |
| 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".