Pure Economic Loss and Agricultural Biotechnology: Comparing Australia, Canada and the Unites States
Bibliographic record
Abstract
Social responses to innovations have varied across time. Regulations have been used in an attempt to limit the uptake of innovations (e.g. coffee), violence has been used (e.g. Luddites), experts have tried to convince individuals that an innovation is dangerous to their health (e.g. train travel) and courts have been used to attempt to control or reduce the market share of an innovation (e.g. Microsoft). In the case of agricultural biotechnology, all of these responses have been employed to varying degrees of success: regulations have been put in place in numerous countries that ban the production of genetically modified (GM) crops; violence has been a tool of NGOs opposed to GM crops as they have destroyed field trials; experts have argued in select instances that the consumption of food products derived from GM crops are dangerous to human health; and those opposed to GM crops in the United States are using the courts to seek injunctions against commercial release of new GM varieties and to argue that regulatory protocols were not properly followed. The most common responses by far, and the focus of this paper, is the employment of regulations and the use of courts. The concept of economic loss in relation to innovation posits that those negatively impacted by the innovation of GM crops are entitled to compensation that offsets the externality. For example, Denmark has established a compensation fund that taxes GM crop adopters, creating a revenue pool to compensate those farmers adversely impacted by the adoption of GM crops in Denmark. In undertaking a thorough assessment of applying economic loss to GM crops, this paper will evaluate the efficiencies of having compensation funded via government efforts versus the use of the court system. The paper will compare and contrast the situation in Australia, Canada and the United States.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".