The Nexus between The Islamic Human Development Index (I-HDI), Unemployment, and Population Growth in Influencing Poverty
Bibliographic record
Abstract
This research investigates the relationship between the Islamic Human Development Index (I-HDI), Unemployment, Population Growth, and their impact on Poverty in G20 countries from 2010 to 2021. A purposive sampling method was used to select eight countries: the United States, Indonesia, the United Kingdom, Italy, Germany, Canada, France, and Turkey. The I-HDI was calculated using five Maqasid Shariah indicators. Unemployment was measured by the national unemployment rate, and Population Growth was represented by the annual population growth rate. A total of 384 panel data points were analyzed using the Fixed Effect Model (FEM) regression technique. The findings reveal a robust negative relationship between I-HDI and the Poverty Rate, indicating that improvements in human development are key drivers in alleviating poverty. In contrast, Unemployment shows a significant positive association with the Poverty Rate, meaning that an increase in unemployment leads to a rise in poverty levels. However, Population Growth does not exhibit a significant effect on the Poverty Rate, suggesting that demographic changes alone are not adequate to explain fluctuations in poverty across the G20 countries. These results offer valuable insights for policymakers focused on enhancing human development and addressing unemployment to effectively combat poverty.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".