Transparency About Governance Contributes to Biobanks' Trustworthiness: Call for Action
Bibliographic record
Abstract
This article examines biobank transparency mechanisms vis-à-vis their public information, as found on the public biobank webpages. Two independent studies about biobank governance in Europe and Canada identified a lack of governance-related information provided by biobanks on their public webpages. This lack of transparency stands in contrast to governance best practice guidelines highlighting the importance of transparency as a principle of good governance. Transparency is especially important as many biobanks are publicly funded, and it contributes to accountability and supports the development of donor trust in biobanks. Empirical evidence shows that the public supports greater transparency about biobank governance. It will be important that information provided online is relevant and accessible for a variety of different stakeholders (e.g. public and private sector scientists and institutions, donors and potential donors, members of the public). Transparency standards, however, need to be proportionate to avoid the situation that only large-scale biobanks can allocate appropriate resources to fulfil them. Implementing adequate standards of transparency about biobanks' governance will increase accountability but also allow current and future participants to make more informed decisions about their participation in biobank activities.
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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 | 0.412 | 0.561 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.015 | 0.071 |
| Scholarly communication | 0.045 | 0.064 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.027 | 0.036 |
| 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; the direct Gemma label and the distilled Codex classifier 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".