Privacy and Biobanking in China: A Case of Policy in Transition
Bibliographic record
Abstract
Disease-based biobanks have operated in hospitals and research institutes in China for decades, and China has recently embarked on a plan to establish further biobank networks with the aim of promoting data sharing among the existing biobanks. Although the Chinese Constitution has only recently begun to recognize individual privacy as a distinct and independent constitutional right, biobanking in China has been loosely regulated under a patchwork of sometimes overlapping laws (such as the Interim Measures for the Administration of Human Genetic Resources) and regulatory instruments, as well as and the policies of individual biobanks and networks of biobanks (such as the Shanghai Biobank Network Guidelines). A Draft Ordinance on Human Genetics Resources is currently being developed that will deal in more detail than previous laws with issues such as management measures, legal liability, and punishment for violations. International data sharing will be tightly regulated under this new law, and individual biobanks' policies such as the Shanghai Guidelines may choose to regulate such sharing even more. In contrast with national regulatory instruments, the Shanghai Guidelines also contain detailed de-identification policies, and explicitly endorse broad 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".