Canada’s Independent Agri-Food “Think-Tank” The Competitiveness Impacts of Canada’s Agricultural Product Review Regulations
Bibliographic record
Abstract
Regulations are necessary for the organization of society. A good regulatory framework protects the health and environment of its citizens, contributes to economic growth, and promotes investments that will improve a nation’s productivity and thus improve the standard of living. A dysfunctional regulatory system, however, hinders investment, productivity and innovation and reduces competitiveness and job opportunity. One aspect of Canada’s regulatory framework is its mechanism for approving new products used for curing or preventing diseases in farm and companion animals. We at the Centre have heard complaints about the approval process across a number of Market Access Regulatory Programs for years. Hence we approached the Canadian Animal Health Institute (CAHI) with a proposal to conduct an economic analysis of the approval process1. The resulting study, the results of which are presented here, had two underlying purposes: 1) Identify the impact of Canada’s product registration system on companies operating in the animal health products industry in Canada, and ultimately, estimate the magnitude of the economic cost to the agri-food sector and the Canadian economy imposed by this system; and 2) Offer some alternative, potentially “optimum, ” solutions to the system and identify the costs/benefits of such a system. The specific objectives of the project were:
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".