Bibliometric analysis of scientific researches on issues of food security of the state
Bibliographic record
Abstract
The article presents a bibliometric analysis of scientific studies devoted to the problems of food security of the state. In the work, we assessed the trends, dynamics and main directions of research development in this field. We analyzed scientific publications, identified key scientific journals, authors, countries and organizations that conduct active research in this field. It was found that scientists from the USA, China and Great Britain made the greatest contribution to the study of this problem, followed by India, Australia, Canada and Germany. The obtained results indicate an increase in the number of publications on this topic. The results of the study showed that interest in the problem of food security is growing significantly, especially in the context of climate challenges and political instability in many regions of the world. The revealed trends indicate that special attention is paid to the issues of ensuring the stability of food supplies, increasing the efficiency of agricultural production and reducing dependence on external sources of supply. The results of this study are relevant and allow us to outline the direction for further study of this issue. During the research, various methods were used to evaluate and visualize scientific activity: analysis of citations to identify influential works and authors, methods of analysis and synthesis, logical method.
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 | 0.010 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.191 | 0.233 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".