Toward the effective implementation of the Biosafety Protocol: a Chinese regulatory capacity-building perspective
Bibliographic record
Abstract
, in establishing and implementing national measures concerning genetically modified organisms (GMOs) that are aligned with the objectives of the Biosafety Protocol. Regulatory capacities of developing countries to address environmental risks caused by GMOs remain to be improved. The article takes China as an example to analyze how regulatory capacity-building activities organized under the Biosafety Protocol contributed to and will further influence China's establishment and implementation of GMO laws and regulations. A four-stage analytical framework is established to examine the interaction between capacity-building activities and the development of China's GMO regulation. China has gradually developed its GMO laws and regulations, with each stage having different regulatory needs and capacity-building efforts. External intervention and endogenous regulatory capacity-building activities mutually strengthened China's implementation of the Biosafety Protocol. Endogenous regulatory capacity-building activities are increasing in enhancing China's GMO regulation. The article concludes by proposing ways to enhance China's regulatory capacities regarding GMOs against the backdrop of adopting the Kunming-Montreal Global Biodiversity Framework and China's Biosecurity Law, involving making laws and regulations on GMOs consistent with the Biosecurity Law and reconsidering the regulatory modes on genome-editing techniques.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".