The Unintended Consequences of Technological Change: Winners and Losers from GM Technologies and the Policy Response in the Organic Food Market
Bibliographic record
Abstract
It is often said that innovations create winners and losers. All innovations are somewhat disruptive, but some have more distributed effects. We have a sense of who the winners are and how much they gain. Yet, how much do losers actually lose? Organic farmers frequently like to publicly announce that they are the losers following the commercialization of genetically modified (GM) crops, yet consumers in search of non-GM products have helped increase demand for organic products, something that would not have occurred in the absence of GM crops. Are organic farmers really losers? This article lays out the argument that were it not for the commercialization of GM crop varieties in the mid-1990s, organic production and food sectors would not be at the level they enjoy today. That is, the commercialization of GM crops has made the organic industry better off than had GM crops not been commercialized. Theoretical modelling of the organic benefits is complemented by supportive market data. The article concludes that in spite of numerous vocal offerings about the adverse impacts suffered by the organic industry due to GM crop production, the organic industry has gained significantly from that which they vociferously criticize.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.011 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".