Examining the Impact of Social Assistance on Poverty: A Bibliometric Analysis
Bibliographic record
Abstract
Does social assistance provide benefits at the level necessary to escape poverty? Our literature search found many studies that sought answers to this question. Therefore, this research aims to investigate the dominant theme in publications related to the impact of social assistance on poverty. This research uses bibliometric analysis using RStudio software with the Scopus database. The data collected was processed using RStudio software to produce visualizations and analyze research trends and topic developments regarding the impact of social assistance on poverty. The most cited articles in 2021 had an annual average citation of 1.9, which shows that the articles in that year were extraordinary. The International Journal of Social Welfare has produced 11 articles and is the most productive source. Since the beginning of 2013, the International Journal of Social Welfare has published more than any other source. In this theme, the United States has the most citations; next, Canada and China are the second and third most cited countries. The United States received the highest 378 citations, while Canada and China received 303 and 280 citations. Barrientos is the most contributing author with the highest H-index score of 6, followed by Walker and Gao with an H-index of 5 and 4, respectively. However, what is most interesting in this finding is that Word cloud Poverty (12%) is the most prominent keyword length. Social assistance was only announced at 2%. Research on the impact of social assistance on poverty is still an exciting topic for future research.
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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 | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.170 | 0.246 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".