Towards Biobank Privacy Regimes in Responsible Innovation Societies: ESBB Conference in Granada 2012
Bibliographic record
Abstract
The creation of socially and technically robust biobank privacy regimes presupposes knowledge of and compliance with legal rules, professional standards of the biomedical community, and state-of-the-art data safety and security measures. The strategies in privacy management and data protection presented in this review show a trend that goes beyond searching for compromises or efforts of balancing scientific demands for efficiency and societal demands for effective privacy regimes. They focus on developing synergies that facilitate cooperative use of biomaterials and data and enhance sample search efficiency for researchers on the one hand, and protect rights and interests of donors and citizens on the other hand. Among the issues covered are: a) ethical sensitivities and public perceptions on privacy in biobanking b) tools and procedures that allow maintenance of the rights and dignity of donors, without jeopardizing legitimate information needs of researchers and autonomy of biobanks, and c) a privacy sensitive framework for sharing of data and biomaterials in the research context.
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".