Analisis Kausalitas Jumlah Penduduk, Pertumbuhan Ekonomi dan Kesejahteraan Masyarakat di Provinsi Jambi
Bibliographic record
Abstract
This study aims to examine the relation of causality of population, economic growth and prosperity of society in jambi province. The data used is panel data during the period 2004-2016. The data analysis tools used are Panel Granger Causality Test and Panel Vector Autoregression (PVAR). This study finds that the welfare of society does not have causality relation to population, vice versa. People's welfare has a causal relationship to economic growth and vice versa. And the population does not have causality relation to economic growth, but economic growth has relation to population. The results of PVAR found that the welfare of the community during a particular period was significantly influenced by the welfare of the community in the previous period, as well as the population and economic growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".