Removing politics from innovations that improve food security
Bibliographic record
Abstract
Genetically modified (GM) organisms and crops have been a feature of food production for over 30 years. Despite extensive science-based risk assessment, the public and many politicians remain concerned with the genetic manipulation of crops, particularly food crops. Many governments have addressed public concern through biosafety legislation and regulatory frameworks that identify and regulate risks to ensure human health and environmental safety. These domestic regulatory frameworks align to international scientific risk assessment methodologies on a case-by-case basis. Regulatory agencies in 70 countries around the world have conducted in excess of 4400 risk assessments, all reaching the same conclusion: GM crops and foods that have been assessed provide no greater risk to human health or the environment than non-GM crops and foods. Yet, while the science regarding the safety of GM crops and food appears conclusive and societal benefits have been globally demonstrated, the use of innovative products have only contributed minimal improvements to global food security. Regrettably, politically-motivated regulatory barriers are currently being implemented with the next genomic innovation, genome editing, the implications of which are also discussed in this article. A decade of reduced global food insecurity was witnessed from 2005 to 2015, but regrettably, the figure has subsequently risen. Why is this the case? Reasons have been attributed to climate variability, biotic and abiotic stresses, lack of access to innovative technologies and political interference in decision making processes. This commentary highlights how political interference in the regulatory approval process of GM crops is adversely affecting the adoption of innovative, yield enhancing crop varieties, thereby limiting food security opportunities in food insecure economies.
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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.018 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 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".