Analysis of genetically modified foods and consumer: 25 years of research indexed in Scopus
Bibliographic record
Abstract
Genetically modified (GM) foods are frequently recognized as an essential source of world food supply, linked to Sustainable Development Goal (SDG) 2: Zero hunger. However, several aspects of the risks and benefits of consuming GM foods have not yet been fully clarified. It is necessary to have the most relevant information to have more accurate legislation that benefits the population. The current research aimed to develop a bibliometric analysis; it also used VOSviewer visualization software to show the evaluation of publications indexed in the Scopus database focused on GM foods and consumers between 1999 and 2023. 979 documents were evaluated. The United States was recognized as the most productive (988 articles); however, Universiteit Gent was the institution with more publications (22), and the European Commission was the funding sponsor with more publications (19). The top institutions originated are from USA, UK, China, Italy and Germany. Nature Biotechnology was the journal with more articles published (28 articles). The study allows for gathering information that helps companies to improve the supply of GM foods and help regulators to generate policies and laws according to scientific evidence. • The USA and Universiteit Gent were the country and institution with more publications. • The top institutions were from the USA, the UK, China, Italy, and Germany. • Nature Biotechnology was the journal with the most articles published (28 articles). • The contribution about consumption of GM foods can contribute to SDG 2 (zero hunger).
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".