Genetically Modified Organisms: Rights To Use Commodity Names and the Lemons Problem
Bibliographic record
Abstract
Genetically modified crops have met some consumer opposition domestically and abroad. This opposition has resulted in variety market and policy reactions with a large potential to disrupt trade and to become a focus of international negotiations. In this paper we consider the spillover from adopters to the non-adopters and non-consumers of GM technology. In the absence of any (organizational) transaction costs the assignment of property right to use the name corn will result in Pareto improving decisions with respect to the introduction of GM technology. However, in the presence of transaction costs the ability to use generic crop names such as corn, the adopters of GM technology have the implicit right and will impose costs on participants in the non-GM marketing channel, by creating a lemons problem. This assignment of property rights can result in the commercial introduction of GM despite potential losses in overall social welfare. If the property rights are reassigned such that the innovators are forced to segregate their GM products through labeling laws, this preempts welfare decreasing technology introduction. The relative efficiency of either allocation of property rights depends on the cost savings, rate of adoption, segregation costs, and consumers preference for the GM crop in question. This suggests that assignment of property rights may be more effective if done on a case-by-case basis rather than a one size fits all 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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.009 | 0.023 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".