Biobank Governance: The Cautionary Tale of Taiwan Biobank
Bibliographic record
Abstract
The importance of biobanks has long been mooted, and multiple models of development and operation can be found as a result of many actors founding biobanks (from institutions starting disease-specific banks to governments starting national population biobanks). Many countries began developing biobanks in the absence of national policies to aid in that formation. Taiwan was one such country. Believing that the unique genetic makeup, distinctive lifestyles, and disease-causing factors of the Taiwanese people deserved study, Taiwan took steps to create Taiwan Biobank. This paper examines Taiwan Biobank’s development and governance and focuses on two matters in particular which generated consternation during the development of Taiwan Biobank: the position adopted in relation to autonomy and ethnicity; and the approach toward transparency and internal governance. It concludes that Taiwan Biobank’s conflict-ridden evolution represents a cautionary tale, an example of how not to develop a flagship resource.
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.049 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".