Sürdürülebilirlik ve Tarım: Araştırma Eğilimleri ve Geleceğe Yönelik Kapsamlı Bir Meta Analiz
Bibliographic record
Abstract
The increase in the number of academic studies on sustainability in agriculture, which are conducted without taking into account future trends and which are repetitive and have limited widespread impact, has slowed down the rate of increase in knowledge in this field and limited the social contribution of academic studies. In order to eliminate this limitation, this study aims to examine the historical and thematic development of sustainability studies in agriculture, to identify knowledge gaps and to reveal future research trends. In the study, bibliometric analysis, thematic analysis and meta-analysis were used to understand the general characteristics and trends of the existing literature focusing on sustainability and agriculture and the development of research in this field. The results showed that scientific research on sustainability in agriculture has been on an upward trend in the last decade. Researchers in the United States, China, Australia, India, India, the United Kingdom, Canada, Italy, the United States of America, China, Australia, India, the United Kingdom, Canada and Italy have been closely collaborating at the international level. To date, the Swedish University of Agricultural Sciences, University of Western Australia, China Agricultural University, Faisalabad Agricultural University and Universiti Putra Malaysia have made the greatest contribution to sustainability in agriculture. The most commonly used keywords in academic studies published in the context of sustainability in agriculture are sustainability, climate change, agriculture, biodiversity, sustainable agriculture.
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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 | 0.049 | 0.078 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.033 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".