Bibliometric Analysis in the Field of Rural Revitalization: Current Status, Progress, and Prospects
Bibliographic record
Abstract
Rural areas play an important role in global sustainable development. In recent years, however, rural development has experienced global crises, such as issues in public education, health care, roads, water and sanitation, along with environmental pollution and a lack of natural resources. It is therefore important to promote rural revitalization in the process of modernization. To objectively reveal the current research status in the field of rural revitalization, we analyzed relevant publications in the Web of Science from 1991 to 2021. The results are as follows: (1) In the past 30 years, the number of publications on rural vitalization has increased, and the period from 1991 to 2021 can be divided into three stages, the initial period (1991-2004), the development period (2005-2016), and the high-yield period (2017-2021). (2) Research on rural revitalization covered 60 countries or regions around the world, involving a total of 3099 authors. China, the United States, and Canada published most of the articles. (3) High-frequency keywords were migration, management, and urbanization, indicating that scientists considered the role of sustainable urban and rural development, policy formulation, and urbanization. We highlight that for the development of the field of rural vitalization, scientists need to further strengthen theoretical research, fully absorb the development achievements of advanced countries and regions, understand the laws and trends of urban and rural development in their own countries, and explore new paths to achieve rural vitalization.
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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 | 0.017 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.122 | 0.238 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".