The quandary of agricultural biotechnology, pure economic loss, and non-adopters: comparing Australia, Canada, and the United States
Bibliographic record
Abstract
Innovations impact societies in a variety of ways. Successful innova tions are utility enhancing, in that they create a higher degree of benefits that offset any of the potential disadvantages of the innovation. Unsuccessful innovations suffer from the reverse, in that they result in more disadvantages than benefits and therefore, are ultimately rejected by society. The innovation of agricultural biotechnology and geneti cally modified (GM) crops has triggered substantial discussion regarding the advan tages and disadvantages of the technology. Numerous financial and economic benefits are starting to be recognized by adopters, but some non-adopters are growing increa singly concerned about their ability to profit given the high levels of GM crop adoption. While some might argue that non-adopters of GM crops are the conventional economic losers of this innovation, the reality is that demand for non-GM products is higher, in large part, because of consumer desires to avoid GM food products. The concept of pure 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. In undertaking a thorough assessment of pure economic loss and GM crops, this article evaluates the logic for, and efficiencies of, having compensation funded via the use of courts versus government regulations. This article considers whether non-adopter rights are developing in the case of GM crops and what governance response mechanism is best suited to those claims. It is concluded that the decision over whether to support or reject an innovation is too important to the larger society as a whole to be decided by the courts. *B.Sc., LL.B. (Hons), Ph.D.; Solicitor of the Supreme Court of Victoria; Faculty of Law, Monash University, Victoria, Australia, email: karinne.ludlow@monash.edu. The authors' re search was supported by a grant from the Academy of the Social Sciences in Australia and The Australian Department of Industry, Innovation, Science and Research. **B.A., Ph.D.; Department of Bioresource Policy, Business and Economics, University of Saskatchewan, Canada, email: stuart.smyth@usask.ca. Smyth's research is supported by VALGEN (Value Addition through Genomics and GE3LS), a project sponsored by the Government of Canada through Genome Canada and Genome Prairie.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".