Statistic tells: the regulatory pendulum of permit trajectories in China’s genetic governance (2021-2024)
Bibliographic record
Abstract
The regulation of human genetic resources in China exhibits distinct characteristics that emphasize national sovereignty. Under this framework, activities such as collection, preservation, export, and international collaboration of human genetic resources require an administrative license. This regulatory system began with the promulgation of the Interim Regulations on the Management of Human Genetic Resources in 1998, evolved with the Regulations on the Management of Human Genetic Resources in 2019 (as amended in 2024), and was further refined by the Implementation Rules of these regulations in 2023. This study examines official government statistics on administrative licensing for human genomic projects conducted between January 2021 and December 2024. Analysis indicates that following the adoption of the Implementation Rules, the overall number of licenses declined by 58.4% from 2023 to 2024 (n = 3,114), while the proportion of revoked licenses increased by 16.2%. Despite geopolitical influences, international cooperation licenses continue to be issued. Furthermore, the primary foreign entities remain multinational corporations headquartered in the United States, whereas domestic applicants are predominantly based in Beijing and Shanghai.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".