Kewarganegaraan dan Kemiskinan : Kebijakan Pemerintah dalam Mengatasi Kemiskinan di Kalangan Warga Negara Asing
Bibliographic record
Abstract
Poverty among foreign nationals is an increasingly pressing global issue that is often overlooked in public policy. This study examines government policies designed to address poverty among migrants and refugees and their impact on their economic well-being. The main focus of the study is to highlight the effectiveness of different policy approaches that have been implemented in different countries and identify best practices that can be adapted to different national contexts. Through an analysis of existing policies in several countries, including Germany and Canada, and relevant case studies, the study finds that approaches based on social and economic integration, employment, and direct assistance can play a significant role in reducing poverty among foreign nationals. The results show that policies that involve collaboration between governments, non-governmental organizations, and local communities are often more effective in achieving the desired outcomes. This study aims to provide evidence-based recommendations for future development policies that are more inclusive and effective. In doing so, it is hoped that this study will contribute to improving the economic well-being of foreign nationals and assist governments in designing policies that are more responsive to the needs of this group.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".