Bibliographic record
Abstract
The European Union is finally coming around to gene-edited seeds For a quarter century, activists such as Vandana Shiva have opposed GM crops that can help feed the world. Now, more than ever, it is time to reject their Luddite demands. In 2021, the European Union announced that it would be reviewing its 2001-era legislation governing genetically modified (GM) organisms, so as to properly account for the recent development of “gene-edited” crops, which are produced using what is known as New Genomic Techniques (NGT). To laypeople, the distinction between the various technology types may seem obscure. But from a scientific point of view, the difference is significant. Genetically modified organisms ‐ also known as transgenic organisms, or GMOs ‐ have been around since the 1990s. Many well-known GMOs have been developed by combining DNA from different types of organisms, a process that has aroused fears of “frankenfoods” (a pejorative term coined three decades ago by Boston College professor Paul Lewis, who wrote that if they want to sell us Frankenfood, perhaps it is time to gather the villagers, light some torches and head to the castle. By contrast, NGTs allow scientists to manipulate specific regions of a genome directly ‐ so as to reduce the need for pesticides, resist disease, boost yields, or enhance resilience in the face of climate change ‐ without importing genetic material from outside sources.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.082 | 0.036 |
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".