Reconceptualizing the Precautionary Principle in China’s GMO Governance
Bibliographic record
Abstract
With the aftermath of COVID-19, China restarted the safety assessment of GM crops. However, the precautionary principle which has guided the environmental legislation in China remains controversy and ambiguous, which has led to the stagnation of GMO industrialization in China. The problem could not be solved without a theoretical and empirical study on the logic operation of the precautionary principle in GMO industrialization. Theoretically, it is found that the precautionary principle, precautionary approach and precautionary measure constitute an organic whole of abstractness and specificness, goal and means; Meanwhile, “Substantial equivalence” should be brought into the system of the precautionary principle Empirically, the rules adopted in GMO industrialization, such as case by case, are exactly the application and expansion of precautionary principle. Based on this, the logic evolution for operation mechanism of precautionary principle can be drawn up: on one side, the precautionary approaches are to be observed by taking advantage of the scientific discovery; on the other side, the technical means shall be adopted as precautionary measures through the legislative proceedings as against scientific uncertainty.
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 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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".