Financial development and poverty reduction in developing countries
Bibliographic record
Abstract
The poverty becomes a serious problem because of the impact it causes. The factors that affect poverty are economic growth, low education, the limitation of natural resources, the limitation of employment opportunities, capital, and family burdens. All of these factors constitute a vicious circle in the problem of poverty. The problems studied are lag-1 investment, lag-2 investment, employment opportunities, and provincial minimum wages and their effects on the poverty rates in Riau Province, Indonesia. The fundamental problem faced by Riau Province today is the high level of poverty amidst government policies that have not met the expectations. The purpose of this study is to analyze government policies in order to reduce the poverty. The research method used was an explanatory study or hypothesis testing study that aims to explain and test hypotheses for the relationship among variables. The relationship described is a causal (cause-effect) relationship. The data were arranged in the form of time series during 1997-2018. The research model was formulated as a linear function based on the Nerlove's Partial Adjustment Model approach and was recursively analyzed using linear regression through the Ordinary Least Square (OLS) method. The findings of this research model are lag-1 investment, lag-2 investment, employment opportunities, and provincial minimum wages have a significant effect on the poverty rate in Riau Province.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".