Analisis Pengaruh Pertumbuhan Penduduk dan Rasio Ketergantungan Terhadap Kemiskinan di Kabupaten Sarolangun
Bibliographic record
Abstract
The purpose of this research is to analyze population growth, dependency ratio and poverty in Sarolangun district, to analyze the influence of population growth, dependency ratio to poverty in Sarolangun district. The method of analysis used in this study is qualitative descriptive analysis and quantitative analysis using an analysis tool that is doubled linear regression. The results of this study is based on statistical test calculation is hypothesis test using t statistic partially shows the variable Dependecy Ratio t count of 3.651386> t table 1, 77 has a positive and real effect on poverty in Sarolangun district, while population growth t count 0.589322 < t table 1.77 has a negative effect on poverty in Sarolangun district. Based on the f statistic test 6.835044> F table 3.49 shows that silmultan (together) that all multiple linear regression coefficients or population growth variables, and Dependecy Ratio jointly affect the Poverty in Sarolangun District.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".