Genetically Modified Organisms and the Future Global Nutrient Supply: Part of the Solution or a New Problem?
Bibliographic record
Abstract
For almost a generation now, scientists and policy makers have enthusiastically advanced genetically modified (GM) crops as a solution to both global food security and, specifically, the micronutrient needs of the hidden hungry. While genetic modification offers the prospect of overcoming technological barriers to food security, the gap between the vision and reality remains large. This chapter examines the impact of GM crops at three levels. Undoubtedly, at the micro level, bio-fortification offers a real opportunity to enhance the availability of micronutrients. However, the inexorable 'research sieve' ruthlessly culls most technical candidates in the agri-food system. GM bio-fortified foods, such as Golden RiceTM, remain only a promise. At the meso level, GM crops have generated benefits for both producers and consumers who have adopted GM crops, but given that the technology has been differentially applied to maize, the average diet for the food insecure has become somewhat less balanced. Finally, while GM crops have increased yields and the global food supply, these have come at the cost of more complex and costly trade and market systems, which impair access and availability. In essence, while biotechnology offers some tantalizing technological prospects, the difficulties of getting the corresponding benefits to the most needy have dampened some of the enthusiasm.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".