The Innovation and Diffusion of Policy: Novelty in the Canadian Regulatory System for Plants with Novel Traits
Bibliographic record
Abstract
In 1993, the Canadian federal government made a decision with respect to the direction that the country would take in regulating agricultural products of biotechnology, commonly referred to as GMOs or GM crops. Following the lead of the United States, Canada adopted the innovative “product-based” approach to regulation, making it necessary for all GM crops to go through the regulatory system in order to gain approval for commercialization. However, the iteration that Canada’s adoption of the policy took differed from the form that the product-based approach took in the United States. Canada created a category of “plants with novel traits”, which is based on the concept of novelty and reflects the idea that products of newer technologies such as recombinant DNA are not fundamentally different than those developed through more conventional means. The United States does not require regulation on novel plants created through conventional means via a regulatory trigger which seeks out plant pathogens, present only in newer, recombinant technologies. As a result, many crops developed through more conventional modification techniques such as mutagenesis are not subjected to the American regulatory system, but are in Canada. The objective of this paper is to determine how Canada and the United States came to adopt the product-based approach to regulation, where the Canadian system began to differ from the American system, and why the Canadian system has not diffused internationally, despite being the most directly implemented representative of the product-based approach. This is accomplished via the application of the policy change, policy diffusion, and policy innovation literatures. Theories of policy change and diffusion are introduced. I trace the history and diffusion of novelty using the historical method, and test the applicability of other diffusion models to the case study in order to determine their predictive power in an international diffusion scenario. The innovation literature is also applied in order to explain how and why the product-based approach to regulation has been incorporated differently at multiple levels of regulatory policy. I conclude with an argument that Canada has lost a “standards war” with the United States for regulatory superiority, in light of lost marketability and a less permissible regulatory landscape, which must prompt us to re-evaluate our regulatory approach.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".