Legal regulation of the use of genomic technologies in agriculture: the evolution of approaches in foreign law
Bibliographic record
Abstract
This article is devoted to a comprehensive analysis of current trends and challenges in the legal regulation of agrobiotechnology in the context of the global transition from classical transgenesis to site-directed genome editing technologies such as CRISPR-Cas9. The relevance of the study is due to the rapid development of these technologies, which offer new opportunities for food security, climate change adaptation and sustainable agricultural development, but face diverse and often outdated legal regimes. The aim of the work is to identify and systematize key models of legal regulation of the turnover of genetically modified organisms (GMOs) and genomic editing products in countries that are leading producers of agricultural products, as well as features, common features and patterns in the development of legal regulation in this area using the example of the agricultural sector. Based on the comparative legal method, the legal systems of the United States, Canada, Brazil, Argentina, the European Union, China, India, and the Russian Federation in this area have been studied. The analysis made it possible to identify three dominant regulatory approaches: a product-oriented model (USA, Canada), a flexible model stimulating innovation (Brazil, Argentina), and a model based on strict application of the precautionary principle (EU, China, India, Russia). Special attention is paid to the legal status of organisms obtained using genome editing technologies that do not contain foreign DNA. Recent legislative initiatives aimed at differentiating their regulation from classical GMOs, in particular, the new EU Regulation on new Genomic techniques (NGTs), have been investigated. In conclusion, the main trends are summarized, which include the transition from regulating the creation process to evaluating the characteristics of the final product, as well as the formation of simplified procedures for genome editing products. It is revealed that the lack of international harmonization in this area remains the main barrier to the development of innovation. It is concluded that it is necessary to develop a detailed and balanced regulatory framework in Russia that would ensure biosafety without hindering scientific and technological progress in the agricultural sphere.
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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 | 0.020 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| 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".