유전자변형(GM)작물의 진흥과 규제에 관한 정책 유형의 분류와 적용: 해외 GM작물 재배국을 중심으로
Bibliographic record
Abstract
이 연구는 GMO 규제에 영향을 미치는 정책 논리를 경제논리와 위험예방논리로 구분하여, 이 두 가지 기준을 바탕으로 4개의 정책 유형을 도출한 후, 각 국의 GMO 규제 정책이 어떤 유형에 해당하는가를 밝히는 것을 목적으로 하였다. 연구자는 GMO정책의 경제논리와 위험예방논리의 강도를 계량화하는 기법을 개발하여 각국의 사례를 측정함으로써 국가 간 규제-진흥의 차이를 밝히고, 분석 결과에 따른 이론적·실천적 시사점을 도출하고자 하였다. 연구 결과, 진흥형의 전형적인 국가로는 미국과 캐나다, 진흥·규제공존형의 전형적인 국가는 EU로 나타났다. GM작물 생산이 적은 국가들은 주로 규제형과 방임형 정책을 사용하는 국가들 중에도 미국이나 캐나다 같이 강한 진흥정책을 채택하는 국가가 있는 반면, 파라과이나 우루과이 및 인도처럼 보다 덜 강한 진흥 정책을 사용하는 국가도 존재하고 있어 각국의 상황에 따른 다양한 정책운용이 이루어지고 있음을 알 수 있었다. This study first invented a typology based on two ideas: danger-prevention and economic growth. The authors developed indicators to measure the level of each nation's GMO promotion or regulation policies, and then empirically analyzed the objective data. The 22 main GMO producing and trading nations were included for the empirical analysis and each nation's GMO policy was finally indicated in a classified policy type. The results showed that the USA and Canada belong to the strong GMO promotion type, while the EU is to the type that combines regulation and promotion policy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".