A Systematic Bibliometric Review of Fiscal Redistribution Policies Addressing Poverty Vulnerability
Bibliographic record
Abstract
The elimination of poverty in all its forms is the first global goal of the United Nations’ 2030 Agenda for Sustainable Development. Achieving this goal is recognized as a long-term process that is complicated by persistent vulnerabilities stemming from factors such as natural disasters, food insecurity, health challenges, educational disparities, and social inequality. This systematic bibliometric review provides a comprehensive survey of the impact of social protection-based policies in mitigating poverty vulnerability, focusing on selected countries and regions, including America, Europe, Oceania, and part of Asia and Africa. Our analysis reveals that 81% of the studies examine poverty vulnerability from a single dimension, predominantly focusing on food security and nutrition (23%), climate change shocks (18%), and health-related vulnerabilities (14%). The geographic distribution indicates that the United Kingdom and the United States lead research in this field, contributing 36 and 32 papers, respectively, followed by China (16 papers), South Africa (15 papers), and Canada (10 papers). The results indicate that these fiscal redistribution policies significantly contribute to reducing poverty and inequality and have positive impacts on other Sustainable Development Goals (SDGs), particularly SDG 1 (No Poverty), SDG 2 (Zero Hunger), SDG 3 (Good Health and Well-being), and SDG 10 (Reduced Inequalities). However, notable gaps remain, especially regarding the integration of these policies with environmental sustainability goals like SDG 13 (Climate Action), which are addressed in only a minority of studies. This study concludes by recommending the adoption of more holistic and integrated policy frameworks that bridge the gap between social protection and environmental sustainability, thereby advancing the entire 2030 Agenda for Sustainable Development.
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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 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.113 | 0.133 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".