Semantic, Pedantic or Paradigm Shift? Recruitment, Retention and Property in Modern Population Biobanking
Bibliographic record
Abstract
Evolving uses of human biological material, including their collection and retention in biobanks and their distribution to diverse projects, are sites of great tension from the human rights perspective. In the medical-legal setting, these rights are often protected and realised through consent practices. In the biobank setting, there endures a widely shared concern over consent, and the many divergent ways it is fashioned and deployed. This article reconsiders consent in the biobank setting, first, addressing the theoretical foundation of consent and its deployment in the broader medical context, second, examining the nature of biobanks and the uncomfortable position of consent therein, and finally, offering a means of approaching recruitment and retention in the biobank setting which is sensitive to originator interests, including human dignity, doing so within the rubric of a property model.
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 | 0.077 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.114 |
| Scholarly communication | 0.013 | 0.036 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.008 |
| 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".