Review of regional poverty research in geography
Bibliographic record
Abstract
Regional poverty is one of the major topics that geographers have paid close attention to and studied for a long time, and the relevant research has provided effective scientific support for the selection of poverty eradication models and strategies, and the formulation of anti-poverty policies.In order to objectively reveal the research characteristics of regional poverty in geographical science, this paper conducts a Bibliometric analysis of relevant research papers from 1976 to 2021 in the Web of Science database.The research results show that: (1) The number of literature published in the field of regional poverty has generally increased.It can be divided into three phases: a germination period before 1991, a growth period from 1992 to 2008, and a high-yield period after 2009.The frequency of citations in the literature indicates that although the field is not yet mature, it intersects with and is favored by multiple disciplines.(2) Sixty-five countries/regions have produced papers in the field of regional poverty, with the top 10 countries ranking in order: the United States, the United Kingdom, Australia, China, Canada, India, Spain, Germany, Italy, and the Netherlands.Among them, the United Kingdom, the United States, China, Australia, Canada, the Netherlands are at the center of international cooperation, with the United States, the United Kingdom and China working most closely together.Among the corresponding authors of different nationalities, Chinese scholars pay the most attention to scientific research cooperation, while German scholars prefer independent research.(3) The key words of regional poverty in geographical research can be summarized into three categories.The first category focuses on the measurement and driving mechanism of regional poverty; the second category focuses on the differences of regional poverty among different groups, and emphasizes the role of government management; the third category often studies regional poverty in combination with social economic development level and employment.(4) The thematic evolution analysis shows that the research on regional poverty was exploratory and scattered before 2008.There was little difference in the degree of attention that researchers paid to the whole, local or individual themes during this period.After 2009, the research themes on regional poverty became more focused, and focuses on the theme with more holistic characteristics.Around on the core issue of regional poverty, research related to economy, development and culture have gradually become a hot spot.According to the existing research, this paper predicts the key directions of the research on regional poverty in the future: strengthening the theoretical research on regional poverty, carrying out integrated research in the field of regional poverty and other disciplines, and continuing to focus on the research themes in the field of regional poverty, and closely linking with poverty reduction measures.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".