Content analysis and evaluation of scientific productions in the subject area of organic food: A scientometric study
Bibliographic record
Abstract
Background and aim: The purpose of organic food production is to keep human health and impose minimum damage to the environment. Regarding the need for organic food production, the aim of the current research is to analyze and investigate the scientific outputs of the organic food field, which are indexed in the Web of Science (WoS). Materials and methods: The present study is descriptive which was carried out, applying a scientific approach. The scientific productions in the field of organic food were extracted from WoS database and all subject areas related to organic food were investigated. The extracted data were analyzed through Bibliometrix and its graphical user interface, Biblioshiny. Bibliometrics package as well as SPSS and Excel were used. Findings: The findings of the research state that strategic diagram is divided into four regions based on the degree of development (density index) and the degree of connection (centrality index). The first quarter represents the central and developmental topics which include organic food consumption. The second quarter of the graph depicts the special topics of this field, which include antioxidant activity, biological density factor, Listeria monocytogenes (the name of a pathogenic bacterium) and Escherichia coli (the name of a bacterium). The topics in the second area have high density and low centrality. The third quarter covers topics that have low density and centrality. These topics are emerging or declining which includes the issue of agricultural networks is the quarter. The fourth quarter represents basic topics which has low density but high centrality. This area includes food management, health quality and nutritional quality of vegetables. Conclusion: The results state that researches in the field of organic food have focused on topics such as attitudes, consumption behaviors and willingness to buy organic food, as well as the quality of organic products.
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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 | 0.018 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.116 | 0.163 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".